dns_malware.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Malware DNS detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of DNS malware\n",
    "\n",
    "Raw data from: CIC-Bell-DNS [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/dns_malware.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>...</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
       "      <td>13891</td>\n",
       "      <td>32844</td>\n",
       "      <td>1.571319e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>6549.301954</td>\n",
       "      <td>0.145137</td>\n",
       "      <td>0.457453</td>\n",
       "      <td>0.283622</td>\n",
       "      <td>-0.175921</td>\n",
       "      <td>5.479869</td>\n",
       "      <td>2.770816</td>\n",
       "      <td>-0.559445</td>\n",
       "      <td>0.762729</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>633</td>\n",
       "      <td>634</td>\n",
       "      <td>21194</td>\n",
       "      <td>54658</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>11184.093823</td>\n",
       "      <td>0.079577</td>\n",
       "      <td>0.483522</td>\n",
       "      <td>0.302905</td>\n",
       "      <td>-0.116849</td>\n",
       "      <td>7.701636</td>\n",
       "      <td>0.524329</td>\n",
       "      <td>-0.617655</td>\n",
       "      <td>0.998333</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>552</td>\n",
       "      <td>551</td>\n",
       "      <td>18276</td>\n",
       "      <td>47900</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>7868.089367</td>\n",
       "      <td>0.037063</td>\n",
       "      <td>0.469737</td>\n",
       "      <td>0.282013</td>\n",
       "      <td>-0.130984</td>\n",
       "      <td>6.103569</td>\n",
       "      <td>0.755797</td>\n",
       "      <td>-0.154165</td>\n",
       "      <td>0.994406</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>453</td>\n",
       "      <td>454</td>\n",
       "      <td>14459</td>\n",
       "      <td>38276</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>8180.896777</td>\n",
       "      <td>0.031301</td>\n",
       "      <td>0.461049</td>\n",
       "      <td>0.276115</td>\n",
       "      <td>-0.177526</td>\n",
       "      <td>5.548300</td>\n",
       "      <td>0.888593</td>\n",
       "      <td>-0.307735</td>\n",
       "      <td>0.970823</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>543</td>\n",
       "      <td>542</td>\n",
       "      <td>17855</td>\n",
       "      <td>45360</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>11846.111252</td>\n",
       "      <td>0.224405</td>\n",
       "      <td>0.481590</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>-0.081200</td>\n",
       "      <td>9.947926</td>\n",
       "      <td>0.568424</td>\n",
       "      <td>-0.113041</td>\n",
       "      <td>0.999875</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6385</th>\n",
       "      <td>6128</td>\n",
       "      <td>6128</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>320</td>\n",
       "      <td>320</td>\n",
       "      <td>11040</td>\n",
       "      <td>25577</td>\n",
       "      <td>1.568126e+09</td>\n",
       "      <td>1.568126e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>8380.201086</td>\n",
       "      <td>0.118317</td>\n",
       "      <td>0.516863</td>\n",
       "      <td>0.297870</td>\n",
       "      <td>0.064206</td>\n",
       "      <td>6.029364</td>\n",
       "      <td>0.344584</td>\n",
       "      <td>-0.098936</td>\n",
       "      <td>0.995989</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6386</th>\n",
       "      <td>6129</td>\n",
       "      <td>6129</td>\n",
       "      <td>8.8.8.8.</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>338487.267143</td>\n",
       "      <td>0.031699</td>\n",
       "      <td>0.180633</td>\n",
       "      <td>0.206478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2769.505348</td>\n",
       "      <td>375.425426</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.028161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>6130</td>\n",
       "      <td>6130</td>\n",
       "      <td>8.8.8.8.</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>16363.165957</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.503960</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>-0.016445</td>\n",
       "      <td>1.289827</td>\n",
       "      <td>0.076152</td>\n",
       "      <td>-0.309324</td>\n",
       "      <td>0.969318</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6388</th>\n",
       "      <td>6131</td>\n",
       "      <td>6131</td>\n",
       "      <td>150.150.150.10.</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-350503.519625</td>\n",
       "      <td>0.846232</td>\n",
       "      <td>0.788379</td>\n",
       "      <td>0.155292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-155.858141</td>\n",
       "      <td>28987.449578</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6389</th>\n",
       "      <td>6132</td>\n",
       "      <td>6132</td>\n",
       "      <td>150.150.150.10.</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>25611.732146</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.489656</td>\n",
       "      <td>0.289671</td>\n",
       "      <td>-0.063120</td>\n",
       "      <td>1.638074</td>\n",
       "      <td>0.167406</td>\n",
       "      <td>-0.992936</td>\n",
       "      <td>0.702701</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6390 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0           DST_IP          SRC_IP  PACKETS  \\\n",
       "0                0           0          8.8.8.8  150.150.150.10      397   \n",
       "1                1           1          8.8.8.8  150.150.150.10      633   \n",
       "2                2           2          8.8.8.8  150.150.150.10      552   \n",
       "3                3           3          8.8.8.8  150.150.150.10      453   \n",
       "4                4           4          8.8.8.8  150.150.150.10      543   \n",
       "...            ...         ...              ...             ...      ...   \n",
       "6385          6128        6128          8.8.8.8  150.150.150.10      320   \n",
       "6386          6129        6129         8.8.8.8.  150.150.150.10        7   \n",
       "6387          6130        6130         8.8.8.8.  150.150.150.10        7   \n",
       "6388          6131        6131  150.150.150.10.         8.8.8.8        6   \n",
       "6389          6132        6132  150.150.150.10.         8.8.8.8        6   \n",
       "\n",
       "      PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  ...  \\\n",
       "0             396  13891      32844  1.571319e+09  1.571320e+09  ...   \n",
       "1             634  21194      54658  1.571320e+09  1.571320e+09  ...   \n",
       "2             551  18276      47900  1.571320e+09  1.571320e+09  ...   \n",
       "3             454  14459      38276  1.571320e+09  1.571321e+09  ...   \n",
       "4             542  17855      45360  1.571321e+09  1.571321e+09  ...   \n",
       "...           ...    ...        ...           ...           ...  ...   \n",
       "6385          320  11040      25577  1.568126e+09  1.568126e+09  ...   \n",
       "6386            0     66          0  1.566519e+09  1.566519e+09  ...   \n",
       "6387            0     66          0  1.566519e+09  1.566519e+09  ...   \n",
       "6388            0    868          0  1.566519e+09  1.566519e+09  ...   \n",
       "6389            0    868          0  1.566519e+09  1.566519e+09  ...   \n",
       "\n",
       "      SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  SPECTRAL_SPREAD  \\\n",
       "0           6549.301954          0.145137           0.457453         0.283622   \n",
       "1          11184.093823          0.079577           0.483522         0.302905   \n",
       "2           7868.089367          0.037063           0.469737         0.282013   \n",
       "3           8180.896777          0.031301           0.461049         0.276115   \n",
       "4          11846.111252          0.224405           0.481590         0.289643   \n",
       "...                 ...               ...                ...              ...   \n",
       "6385        8380.201086          0.118317           0.516863         0.297870   \n",
       "6386      338487.267143          0.031699           0.180633         0.206478   \n",
       "6387       16363.165957          0.006667           0.503960         0.290931   \n",
       "6388     -350503.519625          0.846232           0.788379         0.155292   \n",
       "6389       25611.732146          0.006667           0.489656         0.289671   \n",
       "\n",
       "      SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  \\\n",
       "0          -0.175921        5.479869       2.770816           -0.559445   \n",
       "1          -0.116849        7.701636       0.524329           -0.617655   \n",
       "2          -0.130984        6.103569       0.755797           -0.154165   \n",
       "3          -0.177526        5.548300       0.888593           -0.307735   \n",
       "4          -0.081200        9.947926       0.568424           -0.113041   \n",
       "...              ...             ...            ...                 ...   \n",
       "6385        0.064206        6.029364       0.344584           -0.098936   \n",
       "6386             NaN     2769.505348     375.425426           -0.000199   \n",
       "6387       -0.016445        1.289827       0.076152           -0.309324   \n",
       "6388             NaN     -155.858141   28987.449578            0.000199   \n",
       "6389       -0.063120        1.638074       0.167406           -0.992936   \n",
       "\n",
       "      PERIODICITY_SCDF  LABEL  \n",
       "0             0.762729   True  \n",
       "1             0.998333   True  \n",
       "2             0.994406   True  \n",
       "3             0.970823   True  \n",
       "4             0.999875   True  \n",
       "...                ...    ...  \n",
       "6385          0.995989  False  \n",
       "6386          0.028161  False  \n",
       "6387          0.969318  False  \n",
       "6388          0.000373  False  \n",
       "6389          0.702701  False  \n",
       "\n",
       "[6390 rows x 85 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "malware: 257\tclear: 6133\n"
     ]
    }
   ],
   "source": [
    "print(f\"malware: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in frequency_features: \n",
    "        df.replace([np.inf], -1, inplace=True)\n",
    "        df.replace([-np.inf], -1, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = -1\n",
    "    else:\n",
    "        df.replace([np.inf], 0, inplace=True)\n",
    "        df.replace([-np.inf], 0, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
       "      <td>13891</td>\n",
       "      <td>32844</td>\n",
       "      <td>1.571319e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>46177</td>\n",
       "      <td>118</td>\n",
       "      <td>103.0</td>\n",
       "      <td>32.480764</td>\n",
       "      <td>1056</td>\n",
       "      <td>-0.568307</td>\n",
       "      <td>92.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>85</td>\n",
       "      <td>243</td>\n",
       "      <td>158</td>\n",
       "      <td>92</td>\n",
       "      <td>27.526071</td>\n",
       "      <td>28</td>\n",
       "      <td>23.728814</td>\n",
       "      <td>123.285176</td>\n",
       "      <td>0.634300</td>\n",
       "      <td>0.359395</td>\n",
       "      <td>0.800474</td>\n",
       "      <td>7.727651</td>\n",
       "      <td>2.085956</td>\n",
       "      <td>2.726882e-05</td>\n",
       "      <td>2.732052e-05</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>2.699452</td>\n",
       "      <td>5.743945</td>\n",
       "      <td>0.596391</td>\n",
       "      <td>0.622384</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698613</td>\n",
       "      <td>0.515014</td>\n",
       "      <td>0.494923</td>\n",
       "      <td>0.543803</td>\n",
       "      <td>0.758422</td>\n",
       "      <td>163.085761</td>\n",
       "      <td>176.349766</td>\n",
       "      <td>90.306549</td>\n",
       "      <td>245.439405</td>\n",
       "      <td>299.898332</td>\n",
       "      <td>0.378660</td>\n",
       "      <td>0.036665</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>5.005048</td>\n",
       "      <td>0.075656</td>\n",
       "      <td>False</td>\n",
       "      <td>1.795455</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494923</td>\n",
       "      <td>28487</td>\n",
       "      <td>0.500631</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.568211e-06</td>\n",
       "      <td>0.026409</td>\n",
       "      <td>0.705179</td>\n",
       "      <td>0.145733</td>\n",
       "      <td>0.004819</td>\n",
       "      <td>0.004285</td>\n",
       "      <td>0.00117</td>\n",
       "      <td>24.095953</td>\n",
       "      <td>172.716474</td>\n",
       "      <td>2.250293e+04</td>\n",
       "      <td>6549.301954</td>\n",
       "      <td>0.145137</td>\n",
       "      <td>0.457453</td>\n",
       "      <td>0.283622</td>\n",
       "      <td>-0.175921</td>\n",
       "      <td>5.479869</td>\n",
       "      <td>2.770816</td>\n",
       "      <td>-0.559445</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>633</td>\n",
       "      <td>634</td>\n",
       "      <td>21194</td>\n",
       "      <td>54658</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>46177</td>\n",
       "      <td>119</td>\n",
       "      <td>103.0</td>\n",
       "      <td>33.689761</td>\n",
       "      <td>1136</td>\n",
       "      <td>-0.558716</td>\n",
       "      <td>92.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>85</td>\n",
       "      <td>254</td>\n",
       "      <td>169</td>\n",
       "      <td>91</td>\n",
       "      <td>28.310724</td>\n",
       "      <td>29</td>\n",
       "      <td>24.369748</td>\n",
       "      <td>124.510483</td>\n",
       "      <td>0.624309</td>\n",
       "      <td>0.374901</td>\n",
       "      <td>0.831113</td>\n",
       "      <td>7.539383</td>\n",
       "      <td>1.969270</td>\n",
       "      <td>2.268440e-05</td>\n",
       "      <td>2.271130e-05</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.213281</td>\n",
       "      <td>5.936724</td>\n",
       "      <td>0.575978</td>\n",
       "      <td>0.630386</td>\n",
       "      <td>False</td>\n",
       "      <td>0.687056</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.657049</td>\n",
       "      <td>0.562057</td>\n",
       "      <td>0.681110</td>\n",
       "      <td>168.496946</td>\n",
       "      <td>175.223572</td>\n",
       "      <td>110.381471</td>\n",
       "      <td>218.344563</td>\n",
       "      <td>299.786230</td>\n",
       "      <td>0.236798</td>\n",
       "      <td>0.037516</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>5.005074</td>\n",
       "      <td>0.047312</td>\n",
       "      <td>False</td>\n",
       "      <td>1.935229</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.657049</td>\n",
       "      <td>39553</td>\n",
       "      <td>0.499605</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.757381e-08</td>\n",
       "      <td>0.008490</td>\n",
       "      <td>0.972783</td>\n",
       "      <td>0.355128</td>\n",
       "      <td>0.001102</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.511692</td>\n",
       "      <td>50.588937</td>\n",
       "      <td>5.137935e+04</td>\n",
       "      <td>11184.093823</td>\n",
       "      <td>0.079577</td>\n",
       "      <td>0.483522</td>\n",
       "      <td>0.302905</td>\n",
       "      <td>-0.116849</td>\n",
       "      <td>7.701636</td>\n",
       "      <td>0.524329</td>\n",
       "      <td>-0.617655</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>552</td>\n",
       "      <td>551</td>\n",
       "      <td>18276</td>\n",
       "      <td>47900</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>46177</td>\n",
       "      <td>119</td>\n",
       "      <td>101.0</td>\n",
       "      <td>33.241540</td>\n",
       "      <td>1106</td>\n",
       "      <td>-0.563305</td>\n",
       "      <td>92.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>85</td>\n",
       "      <td>248</td>\n",
       "      <td>163</td>\n",
       "      <td>91</td>\n",
       "      <td>27.934067</td>\n",
       "      <td>30</td>\n",
       "      <td>25.210084</td>\n",
       "      <td>124.511936</td>\n",
       "      <td>0.611967</td>\n",
       "      <td>0.387126</td>\n",
       "      <td>0.842320</td>\n",
       "      <td>7.701208</td>\n",
       "      <td>1.920872</td>\n",
       "      <td>2.712556e-05</td>\n",
       "      <td>2.716252e-05</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>2.246711</td>\n",
       "      <td>5.758462</td>\n",
       "      <td>0.569738</td>\n",
       "      <td>0.609222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.689937</td>\n",
       "      <td>0.644237</td>\n",
       "      <td>0.717148</td>\n",
       "      <td>0.386026</td>\n",
       "      <td>0.785041</td>\n",
       "      <td>115.716338</td>\n",
       "      <td>87.535478</td>\n",
       "      <td>28.168263</td>\n",
       "      <td>194.447774</td>\n",
       "      <td>299.763384</td>\n",
       "      <td>0.272018</td>\n",
       "      <td>0.035593</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>5.005108</td>\n",
       "      <td>0.054348</td>\n",
       "      <td>False</td>\n",
       "      <td>1.898367</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.717148</td>\n",
       "      <td>29873</td>\n",
       "      <td>0.500453</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.624250e-08</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>0.590151</td>\n",
       "      <td>0.435985</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>6.576570</td>\n",
       "      <td>59.115688</td>\n",
       "      <td>2.997055e+04</td>\n",
       "      <td>7868.089367</td>\n",
       "      <td>0.037063</td>\n",
       "      <td>0.469737</td>\n",
       "      <td>0.282013</td>\n",
       "      <td>-0.130984</td>\n",
       "      <td>6.103569</td>\n",
       "      <td>0.755797</td>\n",
       "      <td>-0.154165</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>453</td>\n",
       "      <td>454</td>\n",
       "      <td>14459</td>\n",
       "      <td>38276</td>\n",
       "      <td>1.571320e+09</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>46177</td>\n",
       "      <td>118</td>\n",
       "      <td>97.0</td>\n",
       "      <td>33.196385</td>\n",
       "      <td>1102</td>\n",
       "      <td>-0.560884</td>\n",
       "      <td>91.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>84</td>\n",
       "      <td>248</td>\n",
       "      <td>164</td>\n",
       "      <td>88</td>\n",
       "      <td>28.132530</td>\n",
       "      <td>30</td>\n",
       "      <td>25.423729</td>\n",
       "      <td>122.715279</td>\n",
       "      <td>0.619625</td>\n",
       "      <td>0.380375</td>\n",
       "      <td>0.903713</td>\n",
       "      <td>7.741807</td>\n",
       "      <td>0.947421</td>\n",
       "      <td>3.887855e-06</td>\n",
       "      <td>3.894298e-06</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>2.561905</td>\n",
       "      <td>5.583488</td>\n",
       "      <td>0.568296</td>\n",
       "      <td>0.668806</td>\n",
       "      <td>False</td>\n",
       "      <td>0.717200</td>\n",
       "      <td>0.243337</td>\n",
       "      <td>0.419881</td>\n",
       "      <td>0.503319</td>\n",
       "      <td>0.762210</td>\n",
       "      <td>150.313386</td>\n",
       "      <td>139.681527</td>\n",
       "      <td>78.638416</td>\n",
       "      <td>244.186947</td>\n",
       "      <td>298.644213</td>\n",
       "      <td>0.329629</td>\n",
       "      <td>0.035728</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>5.005081</td>\n",
       "      <td>0.065859</td>\n",
       "      <td>False</td>\n",
       "      <td>1.843267</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.419881</td>\n",
       "      <td>27209</td>\n",
       "      <td>0.499449</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.178698e-07</td>\n",
       "      <td>0.009245</td>\n",
       "      <td>0.908613</td>\n",
       "      <td>0.600879</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.00043</td>\n",
       "      <td>8.331683</td>\n",
       "      <td>71.947076</td>\n",
       "      <td>2.907222e+04</td>\n",
       "      <td>8180.896777</td>\n",
       "      <td>0.031301</td>\n",
       "      <td>0.461049</td>\n",
       "      <td>0.276115</td>\n",
       "      <td>-0.177526</td>\n",
       "      <td>5.548300</td>\n",
       "      <td>0.888593</td>\n",
       "      <td>-0.307735</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>543</td>\n",
       "      <td>542</td>\n",
       "      <td>17855</td>\n",
       "      <td>45360</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>1.571321e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>46177</td>\n",
       "      <td>118</td>\n",
       "      <td>101.0</td>\n",
       "      <td>32.186954</td>\n",
       "      <td>1036</td>\n",
       "      <td>-0.571375</td>\n",
       "      <td>92.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>84</td>\n",
       "      <td>271</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>27.277080</td>\n",
       "      <td>28</td>\n",
       "      <td>23.728814</td>\n",
       "      <td>122.563972</td>\n",
       "      <td>0.620276</td>\n",
       "      <td>0.379724</td>\n",
       "      <td>0.900986</td>\n",
       "      <td>7.860328</td>\n",
       "      <td>1.226098</td>\n",
       "      <td>7.877266e-06</td>\n",
       "      <td>7.888175e-06</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>3.047176</td>\n",
       "      <td>5.787525</td>\n",
       "      <td>0.573961</td>\n",
       "      <td>0.628885</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698618</td>\n",
       "      <td>0.041533</td>\n",
       "      <td>0.475653</td>\n",
       "      <td>0.560376</td>\n",
       "      <td>0.695944</td>\n",
       "      <td>166.938134</td>\n",
       "      <td>206.170856</td>\n",
       "      <td>98.572610</td>\n",
       "      <td>244.043553</td>\n",
       "      <td>297.903898</td>\n",
       "      <td>0.274819</td>\n",
       "      <td>0.036075</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>5.005033</td>\n",
       "      <td>0.054909</td>\n",
       "      <td>False</td>\n",
       "      <td>1.894834</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.475653</td>\n",
       "      <td>47682</td>\n",
       "      <td>0.500461</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685866e-07</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.338043</td>\n",
       "      <td>0.225001</td>\n",
       "      <td>0.001028</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>5.137548</td>\n",
       "      <td>47.734085</td>\n",
       "      <td>6.366841e+04</td>\n",
       "      <td>11846.111252</td>\n",
       "      <td>0.224405</td>\n",
       "      <td>0.481590</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>-0.081200</td>\n",
       "      <td>9.947926</td>\n",
       "      <td>0.568424</td>\n",
       "      <td>-0.113041</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6385</th>\n",
       "      <td>6128</td>\n",
       "      <td>6128</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>320</td>\n",
       "      <td>320</td>\n",
       "      <td>11040</td>\n",
       "      <td>25577</td>\n",
       "      <td>1.568126e+09</td>\n",
       "      <td>1.568126e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>58075</td>\n",
       "      <td>117</td>\n",
       "      <td>105.0</td>\n",
       "      <td>29.171904</td>\n",
       "      <td>851</td>\n",
       "      <td>-0.600855</td>\n",
       "      <td>94.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>86</td>\n",
       "      <td>218</td>\n",
       "      <td>132</td>\n",
       "      <td>93</td>\n",
       "      <td>24.933252</td>\n",
       "      <td>25</td>\n",
       "      <td>21.367521</td>\n",
       "      <td>120.785638</td>\n",
       "      <td>0.634375</td>\n",
       "      <td>0.360938</td>\n",
       "      <td>0.822709</td>\n",
       "      <td>8.432771</td>\n",
       "      <td>1.229407</td>\n",
       "      <td>8.622759e-06</td>\n",
       "      <td>8.643029e-06</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>2.527719</td>\n",
       "      <td>6.019677</td>\n",
       "      <td>0.645754</td>\n",
       "      <td>0.706716</td>\n",
       "      <td>False</td>\n",
       "      <td>0.653125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868289</td>\n",
       "      <td>0.556407</td>\n",
       "      <td>0.645497</td>\n",
       "      <td>74.001959</td>\n",
       "      <td>80.081576</td>\n",
       "      <td>35.894531</td>\n",
       "      <td>104.844510</td>\n",
       "      <td>132.999575</td>\n",
       "      <td>0.208137</td>\n",
       "      <td>0.054583</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>4.968214</td>\n",
       "      <td>0.041894</td>\n",
       "      <td>False</td>\n",
       "      <td>1.978091</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.868289</td>\n",
       "      <td>31116</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.049484e-07</td>\n",
       "      <td>0.007884</td>\n",
       "      <td>0.218445</td>\n",
       "      <td>0.119509</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.00034</td>\n",
       "      <td>6.538138</td>\n",
       "      <td>59.100107</td>\n",
       "      <td>3.394151e+04</td>\n",
       "      <td>8380.201086</td>\n",
       "      <td>0.118317</td>\n",
       "      <td>0.516863</td>\n",
       "      <td>0.297870</td>\n",
       "      <td>0.064206</td>\n",
       "      <td>6.029364</td>\n",
       "      <td>0.344584</td>\n",
       "      <td>-0.098936</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6386</th>\n",
       "      <td>6129</td>\n",
       "      <td>6129</td>\n",
       "      <td>8.8.8.8.</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>53737</td>\n",
       "      <td>69</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.093477</td>\n",
       "      <td>259</td>\n",
       "      <td>-0.621746</td>\n",
       "      <td>60.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>60</td>\n",
       "      <td>93</td>\n",
       "      <td>33</td>\n",
       "      <td>60</td>\n",
       "      <td>23.323880</td>\n",
       "      <td>13</td>\n",
       "      <td>18.840580</td>\n",
       "      <td>71.011066</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.559233</td>\n",
       "      <td>9.134136</td>\n",
       "      <td>0.475024</td>\n",
       "      <td>1.028190e-04</td>\n",
       "      <td>1.332687e-04</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>1.482977</td>\n",
       "      <td>0.863121</td>\n",
       "      <td>0.307450</td>\n",
       "      <td>0.402236</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.342008</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.109573</td>\n",
       "      <td>0.050556</td>\n",
       "      <td>0.025013</td>\n",
       "      <td>0.295354</td>\n",
       "      <td>0.320381</td>\n",
       "      <td>0.053397</td>\n",
       "      <td>0.025020</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.244692</td>\n",
       "      <td>0.218221</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.739651e+01</td>\n",
       "      <td>170.373206</td>\n",
       "      <td>0.031897</td>\n",
       "      <td>0.031699</td>\n",
       "      <td>0.061518</td>\n",
       "      <td>2.443385</td>\n",
       "      <td>0.04573</td>\n",
       "      <td>307.587790</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.360949e+07</td>\n",
       "      <td>338487.267143</td>\n",
       "      <td>0.031699</td>\n",
       "      <td>0.180633</td>\n",
       "      <td>0.206478</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2769.505348</td>\n",
       "      <td>375.425426</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>6130</td>\n",
       "      <td>6130</td>\n",
       "      <td>8.8.8.8.</td>\n",
       "      <td>150.150.150.10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>49281</td>\n",
       "      <td>69</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.093477</td>\n",
       "      <td>259</td>\n",
       "      <td>-0.621746</td>\n",
       "      <td>60.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>60</td>\n",
       "      <td>93</td>\n",
       "      <td>33</td>\n",
       "      <td>60</td>\n",
       "      <td>23.323880</td>\n",
       "      <td>13</td>\n",
       "      <td>18.840580</td>\n",
       "      <td>71.011066</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.559233</td>\n",
       "      <td>9.134136</td>\n",
       "      <td>0.475024</td>\n",
       "      <td>1.028190e-04</td>\n",
       "      <td>1.332687e-04</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>1.482977</td>\n",
       "      <td>0.863121</td>\n",
       "      <td>0.307450</td>\n",
       "      <td>0.402236</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.537897</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.174689</td>\n",
       "      <td>0.274076</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.299878</td>\n",
       "      <td>0.324763</td>\n",
       "      <td>0.054127</td>\n",
       "      <td>0.024923</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.249081</td>\n",
       "      <td>0.217308</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.792658e-01</td>\n",
       "      <td>0.239410</td>\n",
       "      <td>0.315991</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.185614</td>\n",
       "      <td>0.008290</td>\n",
       "      <td>0.17926</td>\n",
       "      <td>928.071595</td>\n",
       "      <td>2253.584260</td>\n",
       "      <td>8.962109e+04</td>\n",
       "      <td>16363.165957</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.503960</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>-0.016445</td>\n",
       "      <td>1.289827</td>\n",
       "      <td>0.076152</td>\n",
       "      <td>-0.309324</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6388</th>\n",
       "      <td>6131</td>\n",
       "      <td>6131</td>\n",
       "      <td>150.150.150.10.</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>53737</td>\n",
       "      <td>53</td>\n",
       "      <td>204</td>\n",
       "      <td>60.0</td>\n",
       "      <td>231.231486</td>\n",
       "      <td>53468</td>\n",
       "      <td>0.062568</td>\n",
       "      <td>60.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>60</td>\n",
       "      <td>584</td>\n",
       "      <td>524</td>\n",
       "      <td>60</td>\n",
       "      <td>113.348768</td>\n",
       "      <td>192</td>\n",
       "      <td>94.117647</td>\n",
       "      <td>294.015873</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.622753</td>\n",
       "      <td>2.387218</td>\n",
       "      <td>0.252187</td>\n",
       "      <td>5.392223e-08</td>\n",
       "      <td>7.383605e-08</td>\n",
       "      <td>-0.116279</td>\n",
       "      <td>1.409161</td>\n",
       "      <td>1.251629</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>0.452722</td>\n",
       "      <td>False</td>\n",
       "      <td>0.592635</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.378440</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.111779</td>\n",
       "      <td>0.054600</td>\n",
       "      <td>0.024995</td>\n",
       "      <td>0.270155</td>\n",
       "      <td>0.295367</td>\n",
       "      <td>0.059073</td>\n",
       "      <td>0.025212</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>0.215555</td>\n",
       "      <td>0.274053</td>\n",
       "      <td>False</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.331866e+04</td>\n",
       "      <td>459.635542</td>\n",
       "      <td>0.846033</td>\n",
       "      <td>0.846232</td>\n",
       "      <td>-2.949063</td>\n",
       "      <td>188.859888</td>\n",
       "      <td>-0.30262</td>\n",
       "      <td>-14745.317102</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.471157e+07</td>\n",
       "      <td>-350503.519625</td>\n",
       "      <td>0.846232</td>\n",
       "      <td>0.788379</td>\n",
       "      <td>0.155292</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-155.858141</td>\n",
       "      <td>28987.449578</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6389</th>\n",
       "      <td>6132</td>\n",
       "      <td>6132</td>\n",
       "      <td>150.150.150.10.</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>1.566519e+09</td>\n",
       "      <td>49281</td>\n",
       "      <td>53</td>\n",
       "      <td>204</td>\n",
       "      <td>60.0</td>\n",
       "      <td>231.231486</td>\n",
       "      <td>53468</td>\n",
       "      <td>0.062568</td>\n",
       "      <td>60.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>60</td>\n",
       "      <td>584</td>\n",
       "      <td>524</td>\n",
       "      <td>60</td>\n",
       "      <td>113.348768</td>\n",
       "      <td>192</td>\n",
       "      <td>94.117647</td>\n",
       "      <td>294.015873</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.622753</td>\n",
       "      <td>2.387218</td>\n",
       "      <td>0.252187</td>\n",
       "      <td>5.392223e-08</td>\n",
       "      <td>7.383605e-08</td>\n",
       "      <td>-0.116279</td>\n",
       "      <td>1.409161</td>\n",
       "      <td>1.251629</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>0.452722</td>\n",
       "      <td>False</td>\n",
       "      <td>0.592635</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624160</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.187133</td>\n",
       "      <td>0.274142</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>0.274763</td>\n",
       "      <td>0.299816</td>\n",
       "      <td>0.059963</td>\n",
       "      <td>0.025016</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>0.224174</td>\n",
       "      <td>0.267485</td>\n",
       "      <td>False</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.250216e-01</td>\n",
       "      <td>0.392427</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.006865</td>\n",
       "      <td>0.239566</td>\n",
       "      <td>0.017986</td>\n",
       "      <td>0.24111</td>\n",
       "      <td>1197.829396</td>\n",
       "      <td>2464.960326</td>\n",
       "      <td>1.812926e+05</td>\n",
       "      <td>25611.732146</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.489656</td>\n",
       "      <td>0.289671</td>\n",
       "      <td>-0.063120</td>\n",
       "      <td>1.638074</td>\n",
       "      <td>0.167406</td>\n",
       "      <td>-0.992936</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6390 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0           DST_IP          SRC_IP  PACKETS  \\\n",
       "0                0           0          8.8.8.8  150.150.150.10      397   \n",
       "1                1           1          8.8.8.8  150.150.150.10      633   \n",
       "2                2           2          8.8.8.8  150.150.150.10      552   \n",
       "3                3           3          8.8.8.8  150.150.150.10      453   \n",
       "4                4           4          8.8.8.8  150.150.150.10      543   \n",
       "...            ...         ...              ...             ...      ...   \n",
       "6385          6128        6128          8.8.8.8  150.150.150.10      320   \n",
       "6386          6129        6129         8.8.8.8.  150.150.150.10        7   \n",
       "6387          6130        6130         8.8.8.8.  150.150.150.10        7   \n",
       "6388          6131        6131  150.150.150.10.         8.8.8.8        6   \n",
       "6389          6132        6132  150.150.150.10.         8.8.8.8        6   \n",
       "\n",
       "      PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  \\\n",
       "0             396  13891      32844  1.571319e+09  1.571320e+09        53   \n",
       "1             634  21194      54658  1.571320e+09  1.571320e+09        53   \n",
       "2             551  18276      47900  1.571320e+09  1.571320e+09        53   \n",
       "3             454  14459      38276  1.571320e+09  1.571321e+09        53   \n",
       "4             542  17855      45360  1.571321e+09  1.571321e+09        53   \n",
       "...           ...    ...        ...           ...           ...       ...   \n",
       "6385          320  11040      25577  1.568126e+09  1.568126e+09        53   \n",
       "6386            0     66          0  1.566519e+09  1.566519e+09        53   \n",
       "6387            0     66          0  1.566519e+09  1.566519e+09        53   \n",
       "6388            0    868          0  1.566519e+09  1.566519e+09     53737   \n",
       "6389            0    868          0  1.566519e+09  1.566519e+09     49281   \n",
       "\n",
       "      SRC_PORT  MEAN  MEDIAN       STDEV    VAR  BURSTINESS    Q1     Q3  MIN  \\\n",
       "0        46177   118   103.0   32.480764   1056   -0.568307  92.0  150.0   85   \n",
       "1        46177   119   103.0   33.689761   1136   -0.558716  92.0  152.0   85   \n",
       "2        46177   119   101.0   33.241540   1106   -0.563305  92.0  153.0   85   \n",
       "3        46177   118    97.0   33.196385   1102   -0.560884  91.0  153.0   84   \n",
       "4        46177   118   101.0   32.186954   1036   -0.571375  92.0  152.0   84   \n",
       "...        ...   ...     ...         ...    ...         ...   ...    ...  ...   \n",
       "6385     58075   117   105.0   29.171904    851   -0.600855  94.0  144.0   86   \n",
       "6386     53737    69    60.0   16.093477    259   -0.621746  60.0   76.5   60   \n",
       "6387     49281    69    60.0   16.093477    259   -0.621746  60.0   76.5   60   \n",
       "6388        53   204    60.0  231.231486  53468    0.062568  60.0  318.0   60   \n",
       "6389        53   204    60.0  231.231486  53468    0.062568  60.0  318.0   60   \n",
       "\n",
       "      MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0     243            158    92                 27.526071                  28   \n",
       "1     254            169    91                 28.310724                  29   \n",
       "2     248            163    91                 27.934067                  30   \n",
       "3     248            164    88                 28.132530                  30   \n",
       "4     271            187    89                 27.277080                  28   \n",
       "...   ...            ...   ...                       ...                 ...   \n",
       "6385  218            132    93                 24.933252                  25   \n",
       "6386   93             33    60                 23.323880                  13   \n",
       "6387   93             33    60                 23.323880                  13   \n",
       "6388  584            524    60                113.348768                 192   \n",
       "6389  584            524    60                113.348768                 192   \n",
       "\n",
       "      PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0             23.728814        123.285176            0.634300   \n",
       "1             24.369748        124.510483            0.624309   \n",
       "2             25.210084        124.511936            0.611967   \n",
       "3             25.423729        122.715279            0.619625   \n",
       "4             23.728814        122.563972            0.620276   \n",
       "...                 ...               ...                 ...   \n",
       "6385          21.367521        120.785638            0.634375   \n",
       "6386          18.840580         71.011066            0.714286   \n",
       "6387          18.840580         71.011066            0.714286   \n",
       "6388          94.117647        294.015873            0.666667   \n",
       "6389          94.117647        294.015873            0.666667   \n",
       "\n",
       "      PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0               0.359395              0.800474              7.727651   \n",
       "1               0.374901              0.831113              7.539383   \n",
       "2               0.387126              0.842320              7.701208   \n",
       "3               0.380375              0.903713              7.741807   \n",
       "4               0.379724              0.900986              7.860328   \n",
       "...                  ...                   ...                   ...   \n",
       "6385            0.360938              0.822709              8.432771   \n",
       "6386            0.285714              0.559233              9.134136   \n",
       "6387            0.285714              0.559233              9.134136   \n",
       "6388            0.333333              0.622753              2.387218   \n",
       "6389            0.333333              0.622753              2.387218   \n",
       "\n",
       "      FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                 2.085956                2.726882e-05   \n",
       "1                 1.969270                2.268440e-05   \n",
       "2                 1.920872                2.712556e-05   \n",
       "3                 0.947421                3.887855e-06   \n",
       "4                 1.226098                7.877266e-06   \n",
       "...                    ...                         ...   \n",
       "6385              1.229407                8.622759e-06   \n",
       "6386              0.475024                1.028190e-04   \n",
       "6387              0.475024                1.028190e-04   \n",
       "6388              0.252187                5.392223e-08   \n",
       "6389              0.252187                5.392223e-08   \n",
       "\n",
       "      FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                   2.732052e-05         0.103448  2.699452  5.743945   \n",
       "1                   2.271130e-05         0.100000  3.213281  5.936724   \n",
       "2                   2.716252e-05         0.114754  2.246711  5.758462   \n",
       "3                   3.894298e-06         0.129032  2.561905  5.583488   \n",
       "4                   7.888175e-06         0.133333  3.047176  5.787525   \n",
       "...                          ...              ...       ...       ...   \n",
       "6385                8.643029e-06         0.080000  2.527719  6.019677   \n",
       "6386                1.332687e-04        -0.090909  1.482977  0.863121   \n",
       "6387                1.332687e-04        -0.090909  1.482977  0.863121   \n",
       "6388                7.383605e-08        -0.116279  1.409161  1.251629   \n",
       "6389                7.383605e-08        -0.116279  1.409161  1.251629   \n",
       "\n",
       "      SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0           0.596391        0.622384                  False   0.698613   \n",
       "1           0.575978        0.630386                  False   0.687056   \n",
       "2           0.569738        0.609222                  False   0.689937   \n",
       "3           0.568296        0.668806                  False   0.717200   \n",
       "4           0.573961        0.628885                  False   0.698618   \n",
       "...              ...             ...                    ...        ...   \n",
       "6385        0.645754        0.706716                  False   0.653125   \n",
       "6386        0.307450        0.402236                  False   0.477121   \n",
       "6387        0.307450        0.402236                  False   0.477121   \n",
       "6388        0.484196        0.452722                  False   0.592635   \n",
       "6389        0.484196        0.452722                  False   0.592635   \n",
       "\n",
       "      NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                0.515014          0.494923           0.543803   \n",
       "1                0.175325          0.657049           0.562057   \n",
       "2                0.644237          0.717148           0.386026   \n",
       "3                0.243337          0.419881           0.503319   \n",
       "4                0.041533          0.475653           0.560376   \n",
       "...                   ...               ...                ...   \n",
       "6385             0.000000          0.868289           0.556407   \n",
       "6386             0.000000          0.000000           0.342008   \n",
       "6387             0.000000          0.000000           0.537897   \n",
       "6388             0.000000          0.000000           0.378440   \n",
       "6389             0.000000          0.000000           0.624160   \n",
       "\n",
       "      AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                     0.758422        163.085761          176.349766   \n",
       "1                     0.681110        168.496946          175.223572   \n",
       "2                     0.785041        115.716338           87.535478   \n",
       "3                     0.762210        150.313386          139.681527   \n",
       "4                     0.695944        166.938134          206.170856   \n",
       "...                        ...               ...                 ...   \n",
       "6385                  0.645497         74.001959           80.081576   \n",
       "6386                  0.775000          0.109573            0.050556   \n",
       "6387                  0.775000          0.174689            0.274076   \n",
       "6388                  0.750000          0.111779            0.054600   \n",
       "6389                  0.750000          0.187133            0.274142   \n",
       "\n",
       "      Q1_SCALED_TIME  Q3_SCALED_TIME    DURATION  MEAN_DIFFTIMES  \\\n",
       "0          90.306549      245.439405  299.898332        0.378660   \n",
       "1         110.381471      218.344563  299.786230        0.236798   \n",
       "2          28.168263      194.447774  299.763384        0.272018   \n",
       "3          78.638416      244.186947  298.644213        0.329629   \n",
       "4          98.572610      244.043553  297.903898        0.274819   \n",
       "...              ...             ...         ...             ...   \n",
       "6385       35.894531      104.844510  132.999575        0.208137   \n",
       "6386        0.025013        0.295354    0.320381        0.053397   \n",
       "6387        0.024961        0.299878    0.324763        0.054127   \n",
       "6388        0.024995        0.270155    0.295367        0.059073   \n",
       "6389        0.024952        0.274763    0.299816        0.059963   \n",
       "\n",
       "      MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0             0.036665       0.000025       5.005048               0.075656   \n",
       "1             0.037516       0.000141       5.005074               0.047312   \n",
       "2             0.035593       0.000135       5.005108               0.054348   \n",
       "3             0.035728       0.000057       5.005081               0.065859   \n",
       "4             0.036075       0.000131       5.005033               0.054909   \n",
       "...                ...            ...            ...                    ...   \n",
       "6385          0.054583       0.000185       4.968214               0.041894   \n",
       "6386          0.025020       0.000030       0.244692               0.218221   \n",
       "6387          0.024923       0.000034       0.249081               0.217308   \n",
       "6388          0.025212       0.000560       0.215555               0.274053   \n",
       "6389          0.025016       0.000621       0.224174               0.267485   \n",
       "\n",
       "      SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0          False          1.795455       False        0.0   \n",
       "1          False          1.935229       False        0.0   \n",
       "2          False          1.898367       False        0.0   \n",
       "3          False          1.843267       False        0.0   \n",
       "4          False          1.894834       False        0.0   \n",
       "...          ...               ...         ...        ...   \n",
       "6385       False          1.978091       False        0.0   \n",
       "6386       False          1.333333       False        0.0   \n",
       "6387       False          1.333333       False        0.0   \n",
       "6388       False          1.600000       False        0.0   \n",
       "6389       False          1.600000        True        0.0   \n",
       "\n",
       "      CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                0.494923              28487    0.500631        False    0   \n",
       "1                0.657049              39553    0.499605        False    0   \n",
       "2                0.717148              29873    0.500453        False    0   \n",
       "3                0.419881              27209    0.499449        False    0   \n",
       "4                0.475653              47682    0.500461        False    0   \n",
       "...                   ...                ...         ...          ...  ...   \n",
       "6385             0.868289              31116    0.500000        False    0   \n",
       "6386             0.000000                486    1.000000        False    0   \n",
       "6387             0.000000                486    1.000000        False    0   \n",
       "6388             0.000000               1228    1.000000        False    0   \n",
       "6389             0.000000               1228    1.000000        False    0   \n",
       "\n",
       "      TIME     MIN_POWER   MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0        0  4.568211e-06    0.026409        0.705179        0.145733   \n",
       "1        0  1.757381e-08    0.008490        0.972783        0.355128   \n",
       "2        0  8.624250e-08    0.008028        0.590151        0.435985   \n",
       "3        0  2.178698e-07    0.009245        0.908613        0.600879   \n",
       "4        0  2.685866e-07    0.010222        0.338043        0.225001   \n",
       "...    ...           ...         ...             ...             ...   \n",
       "6385     0  1.049484e-07    0.007884        0.218445        0.119509   \n",
       "6386     0 -1.739651e+01  170.373206        0.031897        0.031699   \n",
       "6387     0  1.792658e-01    0.239410        0.315991        0.006667   \n",
       "6388     0 -1.331866e+04  459.635542        0.846033        0.846232   \n",
       "6389     0  2.250216e-01    0.392427        0.999801        0.006865   \n",
       "\n",
       "      POWER_MEAN   POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0       0.004819    0.004285     0.00117        24.095953        172.716474   \n",
       "1       0.001102    0.001152     0.00000         5.511692         50.588937   \n",
       "2       0.001315    0.001244     0.00003         6.576570         59.115688   \n",
       "3       0.001666    0.001617     0.00043         8.331683         71.947076   \n",
       "4       0.001028    0.001067     0.00005         5.137548         47.734085   \n",
       "...          ...         ...         ...              ...               ...   \n",
       "6385    0.001308    0.001195     0.00034         6.538138         59.100107   \n",
       "6386    0.061518    2.443385     0.04573       307.587790         -1.000000   \n",
       "6387    0.185614    0.008290     0.17926       928.071595       2253.584260   \n",
       "6388   -2.949063  188.859888    -0.30262    -14745.317102         -1.000000   \n",
       "6389    0.239566    0.017986     0.24111      1197.829396       2464.960326   \n",
       "\n",
       "      SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0          2.250293e+04        6549.301954          0.145137   \n",
       "1          5.137935e+04       11184.093823          0.079577   \n",
       "2          2.997055e+04        7868.089367          0.037063   \n",
       "3          2.907222e+04        8180.896777          0.031301   \n",
       "4          6.366841e+04       11846.111252          0.224405   \n",
       "...                 ...                ...               ...   \n",
       "6385       3.394151e+04        8380.201086          0.118317   \n",
       "6386       2.360949e+07      338487.267143          0.031699   \n",
       "6387       8.962109e+04       16363.165957          0.006667   \n",
       "6388       2.471157e+07     -350503.519625          0.846232   \n",
       "6389       1.812926e+05       25611.732146          0.006667   \n",
       "\n",
       "      SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0              0.457453         0.283622       -0.175921        5.479869   \n",
       "1              0.483522         0.302905       -0.116849        7.701636   \n",
       "2              0.469737         0.282013       -0.130984        6.103569   \n",
       "3              0.461049         0.276115       -0.177526        5.548300   \n",
       "4              0.481590         0.289643       -0.081200        9.947926   \n",
       "...                 ...              ...             ...             ...   \n",
       "6385           0.516863         0.297870        0.064206        6.029364   \n",
       "6386           0.180633         0.206478       -1.000000     2769.505348   \n",
       "6387           0.503960         0.290931       -0.016445        1.289827   \n",
       "6388           0.788379         0.155292       -1.000000     -155.858141   \n",
       "6389           0.489656         0.289671       -0.063120        1.638074   \n",
       "\n",
       "      SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0          2.770816           -0.559445              True   True           1  \n",
       "1          0.524329           -0.617655              True   True           1  \n",
       "2          0.755797           -0.154165              True   True           1  \n",
       "3          0.888593           -0.307735              True   True           1  \n",
       "4          0.568424           -0.113041              True   True           1  \n",
       "...             ...                 ...               ...    ...         ...  \n",
       "6385       0.344584           -0.098936              True  False           0  \n",
       "6386     375.425426           -0.000199              True  False           0  \n",
       "6387       0.076152           -0.309324              True  False           0  \n",
       "6388   28987.449578            0.000199              True  False           0  \n",
       "6389       0.167406           -0.992936              True  False           0  \n",
       "\n",
       "[6390 rows x 86 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddos: 257\tclear: 6133\n"
     ]
    }
   ],
   "source": [
    "print(f\"ddos: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6390, 77)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "malware dns: 257\tclear: 6133\n"
     ]
    }
   ],
   "source": [
    "print(f\"malware dns: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 1000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 50, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,18),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 50, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:06<00:00, 15.50trial/s, best loss: -0.85]             \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2763155040569786, 'gamma': 15.173543986313838, 'max_depth': 20.0, 'min_child_weight': 19.0, 'n_estimators': 60.0, 'reg_alpha': 0.0, 'reg_lambda': 0.21648888829488008}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      model.fit(X_train, y_train)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.13 & 96.49 & 82.09 & 88.71 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLgAAAHYCAYAAABOaVGrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACsp0lEQVR4nOzdd1gUV9sG8HuXDiqIiBVUVIqK2EXFioqxgSLYS0zsElFj7JporIkau5jEXiHW1y427CWxJPaCXbFTLMDK+f7g2wnL7lIXWDb37732+r6cOTNzZhh3n3nmnDMyIYQAERERERERERFRPiXP6wYQERERERERERFlBxNcRERERERERESUrzHBRURERERERERE+RoTXERERERERERElK8xwUVERERERERERPkaE1xERERERERERJSvMcFFRERERERERET5GhNcRERERERERESUrzHBRWlKSkrK6yYQpYvXKeUVXntElJuEEHndBKJcwd9Xyiu89vI3JrhSqV69OmQyGaytrREXF6e1XtmyZSGTyXDixIlcbF3ajh49CplMhgoVKmR7W7GxsRgzZgzmzp2rg5ZlTFRUFObNm4dGjRrBwcEBpqamsLW1RcOGDTFnzpw0/x65ITExERMmTEC5cuVgZmaGIkWKYNasWbmyb5lMBplMhsePH+fK/jJK2S6ZTIapU6emW//WrVtSfWNj42zvPykpCb///jt69eqVqfVWrVoFmUyG5s2bZ7sN2uzduxeenp4oWLAgrKysULNmzRzblyZXr16FlZUVZDIZpkyZkmbdM2fOwMzMDCYmJlq/016/fo2QkBC0atUKFStWhJWVFQoWLAgnJyd07doVoaGh+Pz5s8Z1mzRponKtKD9GRkaws7ND3bp1MXPmTHz8+DHbx51bsnrtEVHuu3//vsbvIOVHLpejQIECcHFxQd++fXH79m2d7LdPnz6QyWT48ccfs72ty5cvo2nTpnjy5IlKuT7GoymdO3cOQUFB8PDwgK2tLczMzODo6IjAwEDs3bs3r5uH27dvo3379lLbSpcujbt37+b4fnMjDskKZbuU/y4ePnyY7jrjxo2T1vn666+z3YYXL16gf//+2Lx5c6bWU8Ya69aty3YbNMnL+wClyZMnQyaTwcrKCtevX0+z7rBhwyCTyVC9enWt8dXly5cxZswY1KlTB6VKlYKZmRns7OxQs2ZNjBgxAlevXtW4Xlrfqebm5ihVqhQ6dOiAffv2ZfuYc1NWrz3SL9m/wzQg586dw6VLl2Bubo6YmBisWrUKQ4cOzetm5Yng4GCsWLEiQ0kLXfj9998xbNgwvH//Hra2tqhcuTLq1q2Lp0+f4ty5czhx4gTmzZuHbdu2oXbt2rnSptR++uknTJs2DaampvD29oalpSWqVauWJ23RRxs3bsTEiRPTrLN27Vqd7nP9+vX4+uuv4e3trdPtZtf9+/fh5+eHhIQEeHh4wMXFBWXKlMnVNlSuXBkLFy7EV199hSlTpqBJkyZo1KiRWr2oqCh06tQJCQkJmDdvHry8vNTqzJ8/H+PHj8f79+9hamqKmjVrwsPDAwqFAnfv3sXmzZuxadMmuLu7Y/v27XByctLYJnd3d1StWlX6b4VCgTdv3uD8+fMYO3YsNm/ejIiICBQsWFB3JyKH6Ou1R0Rp8/Pzg5WVlUpZYmIiHj9+jL/++gu3bt3C5s2bcejQIXh6euZRK9XVqFEjX/UqePfuHQYNGoRNmzYBAJydneHp6QljY2PcvHkTYWFhCAsLg5+fH9atW6f2N8ktbdu2xa1bt1C6dGl4e3sjISEh13+v9ZUQAhs3bsTo0aPTrLN+/Xqd7rdr1644fPiwxpglL+nDfcCkSZMQERGBo0ePonPnzjh79iwsLCzU6q1fvx4LFixA4cKFsXXrVrU6r1+/Rt++fbFz504AgL29PapXrw5ra2tER0fjr7/+wrx58/DLL78gKCgIc+fOhZGRkcY2pf5O/fTpEx4+fIgdO3Zg+/btmDJlSrr3B/pCX689yiRBkr59+woAYvLkyQKAcHFxEUlJSRrrlilTRgAQx48fz+VWanfkyBEBQJQvXz7b2+rdu7cAIKZOnaqDlqXtxx9/FACElZWVCAkJEZ8+fVJZ/uTJE+Hr6ysACBsbG3Hnzp0cb5Mmbdq0ka6P3Hb9+nVx/fp1kZiYmOv7TgsAAUAULlxYABB//fWX1rpJSUmiXLlywszMTAAQRkZG2d7/ypUrBQDh7e2dqfXevXsnrl+/Lh4+fJjtNmgSFhYmAAgnJyfx+fPnHNlHRvXo0UMAEKVKlRIvX75UWZaYmCgaNWokAIjAwECN648ZM0YAEKampmLatGkiNjZWrc6tW7eEj4+PACCcnZ3F+/fvVZY3btw4zX870dHRol69egKA+Pbbb7N2oLksq9ceEeW+yMhI6fcqMjJSa72nT5+KBg0aCADC3d1dawyYUbqMpZTtf/TokUr5nTt3xPXr18WHDx+yvQ9d+fDhg3B3dxcARO3atcXp06fV6hw6dEjY29sLAKJdu3Z50EohXr58maHrIifkdBySVcrfNmVcV7Vq1TTrK+89lLHdV199le02KGOGtWvXZmq9Bw8eiOvXr4vo6Ohst0GTvLwPSOnp06fSv51+/fqpLb948aKwsLAQMplM7Nq1S23527dvhZubm3Svu2/fPrXvuqSkJLFlyxZRtGhRAUCMHz9eZXlGvlOPHz8uzMzMhEwmE3///XfWDzgXZfXaI/3CIYr/LyYmBps2bYK5uTlGjBiBKlWq4ObNmzhw4EBeN82gnThxApMmTYKJiQkOHjyI/v37w8zMTKVOyZIlERYWhrp16+Ldu3cYOXJknrT106dPAJKHA+Q2V1dXuLq66mRYX04ICAgAAGzYsEFrnZMnTyIyMhJt27bNrWZpZW1tDVdXVzg4OOTI9pXXioODA+TyvP2aXbp0KVxcXPDkyRP06tVLZf6WkSNHIiIiApUqVcLvv/+utu6hQ4cwc+ZMGBsb43//+x/GjRuHAgUKqNWrWLEidu3ahRo1auDWrVv47bffMtXGQoUK4fvvvwcAhIaGZu4AiYh0pESJEli2bBkA4O+//86VoWrZVb58ebi6umrsxZFXgoOD8ffff6Nu3bo4cuSIxp5wzZo1w759+yCXy/G///0P27dvz/V2Kn+rgdyP7XI6DsmuGjVqoHz58rhy5QquXbumtZ5yOKA+xHaOjo5wdXVFoUKFcmT7eXkfkFKJEiWwdu1ayGQy/PrrryrD6d68eYOOHTvi48ePmDBhAtq0aaO2/jfffIPr16+jSpUqOHv2LHx8fCCTyVTqyGQydOzYEbt374ZcLsdPP/2EV69eZaqdXl5e6NixI4QQCAsLy9rBEmVFXmfY9MXixYsFANGxY0chhBBz584VAESbNm001lf24Dpy5IiYNWuWcHZ2FmZmZsLBwUEMHjxYPH78WG2dxMREMXfuXOHp6Sns7OyEmZmZKFeunPjqq6/EtWvXNO7n2rVrom/fvqJMmTLCxMREFC5cWDRv3lyEhYWp1dXWgwtanvppWidlRj7lJ/XTij///FN06dJFlChRQpiYmIhixYqJwMBA8eeff2o8Dm3atWsnAIghQ4akW3f//v2iVq1aYtiwYUKhUKgsy8p56ty5s3j+/LkYMGCAKF26tDA1NRUODg5iyJAh4vnz51J9ZY++1J/GjRsLIdJ/Qqutt9+OHTuEj4+PtO/ixYuL9u3bi3379qltQ9vfUKFQiGXLlon69euLggULCjMzM1GxYkURHBwsnjx5orYd5ZOJ69evi02bNon69euLAgUKCCsrK+Hl5aXxfKVF2a5jx44JAKJ06dJan3j3799fABA7duzQ2oPrzZs3YurUqaJu3brCxsZGGBsbC1tbW9GkSROxevVqlbrK86rpb5Lyb/zHH38IJycnYWZmJsqXLy8uXryo1vvm8+fPomHDhgKAqF+/vlqvq6NHjwq5XC6MjY3FqVOntJ4Pbf9+kOoJV1avV03HkhGXLl0S5ubmAoD4+eefhRBCbN68WQAQBQsWFNevX9e4nrInw7BhwzK0n/3794uvvvpK7N+/X6U8vR5cQiT3UsT/99LU5MyZM6Jz587Sd07RokVF+/btRXh4uNZtZnadK1euiK5du4oKFSoIMzMzUbhwYdGwYUOxdOlSld6TaV17RKR/MtqDSwghYmNjpbraeh61b99eFC1aVJiYmIjSpUuLL7/8Uty+fVutblrxwZYtW0T79u1FyZIlhampqbCyshJubm7i22+/Veltqy0GUR5H6hhD2Wt37NixGo8vJiZGWFpaCrlcrtJ7SKFQiN9++014eXmJQoUKCXNzc+Hm5iYmTJgg3r17l+Y5SykqKkoYGxsLAOLcuXPp1u/fv7/44osvRGhoqNqyTZs2CW9vb1G4cGFhamoqypQpI7766itx8+ZNtbrKc713715x4MAB0bx5c2FjYyMsLCxEzZo1RUhIiEp8oul7HIBYuXKlECJzsbNSXFyc+P7770WNGjWEtbW1sLS0FC4uLuKbb75R66mVVi/gR48eiW+++UZUqFBBmJqaikKFCokGDRqI5cuXq8W/ymu7bt26IiYmRowaNUo4OTkJU1NTUaxYMdGrV69MjXxI2a6JEycKAGLcuHEa6378+FFYW1uL6tWrS9epph5cFy5cEH369BHly5cXlpaWwszMTDg6OooePXqo9OxRnldtfxPl33jHjh1i0KBBomDBgqJAgQKibdu2Qgj13jdXrlyRepYtXbpUrV1fffWVACCqV6+uNnokpfTuA5Sycr1qO5aMGDt2rAAgChUqJO7duyeSkpJE69atBQDh4+OjcQTBzZs3hUwmE0Daoy5SGj16tBg/frzKv4WMfqeOHj1aABDBwcFqyzJ7D5PVdVauXCkaN24sihcvLkxNTUXp0qVFly5dxJkzZ6Q66V17lL8wwfX/qlatKgCI3bt3CyGSuy2bmZkJuVyu8YdB+cNYqVIlAUDUqVNHdOrUSZQuXVoAEHZ2duKff/5RWUc5zM7a2lp88cUXomPHjqJ8+fICgLC0tBQXLlxQqb9582bpi7lixYqiU6dOolGjRsLIyEgAED169FD58tJFguvly5eie/fuwsnJSQAQHh4eonv37mLLli3SOsuXL5faULVqVeHv7y9q1qwpAAhjY2OxatWqDJ3zV69eSUGQpiAyo7J6nho0aCBKlSolChQoIJo3by7atGkjLC0tpfMRFxcnhEgOQrt37y6KFy8uAIh69eqJ7t27ix9//FEIkbUE17x586Tz1bhxY9GpUydRu3Zt6W+1ZMkSlW1o+hvGxsZKSRlzc3PRokUL4e/vL0qVKiUAiCJFiqidV+WPf2BgoHT9duzYUVSuXFnax7JlyzJ87lO2SznE7OjRo2r1Pn36JAoXLiyqVq0q/SimTnA9evRIlC1bVgAQxYsXF23atBF+fn6iYsWK0n5SdpEODg6W9lm8eHGVv4nyb1yhQgVhbGwsatSoIdq3by8qVqwoEhISNAaW9+/fF9bW1gKAmDt3rlT+5s0b6d/1zJkz0zwfyn8/9evXFwCEvb296N69u+jevbt0w5LV61XbsWTUsmXLBJA81HDnzp3SsWpLaqYMXlJ/l2VWRhJca9asEQA0Bndz584Vcrlc+k4KCAgQdevWldqnKfjO7DqnTp0SFhYWUrDr7+8vvL29hYmJiZRgVErr2iMi/ZOZBNe6desEAGFiYqKW2FHe8MvlclG7dm3h7+8vxYFWVlZqyX1t8YHyxloZAwQEBAgvLy/pd6BixYrSUG9lDKJsf4cOHVR+U1LHGIcPHxYARJkyZTQ+cFqxYoUAIFq2bCmVffz4UbRs2VI6jsaNG4sOHTpIcU+FChXE/fv3M3Suf/nlFwEkD33KqsTERNGpUye1OKlChQoCgLCwsBDbt29XWUd5rgMCAgQAUa5cOeHn56cSW40ZM0aqHxwcLDp06CAtU/5WR0RECCEyn+CKj48XderUEQBEsWLFRJs2bYSvr68oWbKkACCKFi0qHjx4INXXluA6duyYKFSokACSHxr6+/uL5s2bS3FD8+bNVYajKq9tNzc3UaVKFWFmZiYaN24s/Pz8hI2NjRQPanrwrknKdt28eVM6l5ooH5TNnTtXa4Jr1apV0nWt/G1t3ry5FINYWFiIy5cvCyGSH/51795dGn5Xv359lb+J8m/s6uoqzMzMRNu2bUW9evWkqQ00DS9TxtsFCxZUOf+hoaFSuabkdErp3Qdk53rVdiwZkZiYKLy8vAQA4eXlJX766Sfp3/6rV680rvP9998LAKJWrVoZ3o8mGf1ObdasmQAg/vjjD5XyrNzDZGWdYcOGSX+DFi1aiE6dOokqVapI9yH/+9//hBDpX3uUvzDBJYQ4ffq0ACAcHR1Vbiy7desmAM1ZZ2VAIZfLxebNm6Xy+Ph4KRCpXr26VH7q1CkpaEkZMH3+/FkMHDhQABC+vr5S+Y0bN6Qbq4ULF6oEKZcuXZJ+MKdNmyaV6yLBpaQtKDt9+rQwMjISlpaWYs+ePSrLdu3aJczNzYWJiYn0Y5UW5TkxNjbO8txS2TlPAESjRo3Es2fPpGX37t0TRYoUEQDE8uXLVfbl7e0tAPVsfmYTXPHx8aJAgQLCyMhIXLlyRaXu+vXrBQBha2ur8pRO09+wV69e0s17yieDCQkJIjg4WEqwvH37Vlqm/PE3MjJSG18+fPhwAUCULFlS43FokrJdyl6Q/fv3V6u3ZcsWAST3HtKW4FL+e2vXrp1K4iYpKUnMnDlTCkRSnhdtAWLKv/GAAQOkcuW/b23rKc+/hYWFFPD4+/sLIPlpWEbnY1m7dq0A1J/uZfd61XQsmdG5c2dpWwDEyJEjtdZVnovixYtnej+paUtwJSYmiqioKLFu3TpRuHBhUaBAAbV/E4cOHRJAcmIudYB06NAhUbBgQQFArF+/PlvrKG/uUieXr127JgXiKXvMcQ4uovwjvZuxjx8/irt374p58+YJKysrtWSIEP/eEBcrVkzlyb8QQvz6669CJpMJGxsblZhCU3xw4MABAST3Vk3de//69evS903K2FII7bFc6hgjKSlJekh57NgxtWNt0qSJACA2bdoklQ0ePFgAyQ/+UiZCPn36JL7++msBQHh6eqptSxNlb+0+ffpkqL4mkyZNkuLylA9YkpKSxM8//yyA5AfDKRMTynMNQMyaNUvlN3L+/PkCSJ4nKiYmRip/9OiRtE5qmY2dN2zYICUa4uPjpfL4+Hhp7qaUvaE1/Ya8fv1amvtq9OjRKrFxZGSk9DAy5bxLKa/tSpUqqTyUf/nypfSQUFsvrNRSt0uZINTUe71du3bCyMhIPH/+XGOC682bN6JAgQICgNi4caPKum/fvpUeOg0aNEhlmbZ5kJR/Y5lMJk6ePCmVK//WmtZLSkqS5ght0aKFEEKIhw8fSud5w4YNGTovQmi/D8jO9artWDLq0aNH0n2L8ho/f/681votWrTQ+P2WWWl9p3769EncvHlT+l5p1qyZWs/DrNzDZHadx48fC5lMJmxtbdX+HU+fPl3aVkqcg8swMMElhOjTp48AIL7//nuVcuWwK2tra7WJlZUBhaYf8A8fPkiT8ikzv8rAqGHDhmo3yc+ePRMLFiwQBw8elMqUAUKvXr00tnnfvn1S25Q/pLmR4Grfvr0AIH766SeN7ZowYYIAIHr27KlxeUrKc5KdG+jsnCcAGoeG9uvXTwAQQ4cOVSnXVYLrxYsXAoAoUKCAyhe30sKFC0VoaKj4+PGjVJb6b6j80jYyMtL45CkpKUnqYTJr1iypPGUPrtSePHki7Ufbk5/UUrbr5cuXwsTERNja2qr1LPLz8xNGRkbi6dOnWhNcw4cPF61atdI4XC7lkJGUNw8ZSXDdvXtXbXtpJSeUCeqmTZuK3377TQAQJUqUEC9evMjQORFCe4Iru9erpmPJjHfv3knJnWLFiqWZWJ4xY4YAtD/li42NlZ54a/qkpLzu0vpYWlqqBHlKysTTpEmTNLZD2TPNzc0tW+soe2Gk/B5W2rJli1izZo1K93cmuIjyj7SGj2v6Lvr+++/VbjSVPf219XpVDg2cOHGiVKYpPti6davo1KmTSk/hlJQPVWbMmKFSntEElxBCTJ06VQAQX3/9tUrdBw8eSDd8yiFZUVFRwsTERJiZmWns5ZOQkCDKlSsnAIhDhw5pbHNKyiFSWb2Bjo+Pl36nDh8+rLFOly5d1BIjynNdp04dtfqJiYlSD92UoyV0meCaPXu2AKD2+ydE8sOtJUuWqCSJNP2GKG+6tQ15v3btmpDL5cLIyEj6PUp5bad+8CyEENOmTROA5t7RmqRul7JHXuqYWBnzffHFF0IIoTHB9c8//4ju3btrTXYuXLhQAMkPEFNKL8HVsGFDjdvTtt6zZ8+EnZ2dACBWrFgh1cvshPia7gOye71qO5bMUPbKBCBGjBiRZl0XFxcBQCxatEjj8t27d2uN60JCQqR6Gf1ObdCggTQiRikr9zBZWefcuXMCSH7hU8qksxDJDzXmzZsntm3bplLOBJdh+M9PMh8dHY3Q0FAYGRnhq6++UlnWqFEjuLq6Ijo6GmvWrNG4fu/evdXKLCws0KpVKwDAkSNHAAANGjSAmZkZjh8/Dk9PT/z888+4cuUKhBAoXrw4goKC0Lx5c2kbhw8fBgB0795d4359fHxgb2+P6Oho/Pnnn5k/8CxISkqS2tWyZUuNdZSTTIaHh6e7PRMTEwBAQkJCltuUnfNUuHBhuLm5qa1TsmRJAMD79++z3K60FC1aFO7u7oiLi4OHhwfGjx+Po0ePSudh6NChCAgIgLm5udZtHD16FEII1KlTBxUqVFBbLpPJ0LNnTwDJE4WnVr9+fbWy4sWLS5NMZuXY7ezs4OPjgzdv3mDfvn1S+Zs3b7Bnzx40b94cJUqU0Lr+3LlzsXfvXri6ukplcXFxuHDhAlavXi21LT4+PsNtsra2hpOTU6aOY8mSJShbtiyOHDmCgQMHQi6XY/369ShatGimtqNJdq7XrBxLar/++itiY2MBAFFRUViwYIHWukIIAND6WvpPnz5h/fr1Wj+auLu7o3v37tKna9euaNu2LZycnPDhwwe0bNkSy5cvl+p//vwZx44dA6D9nHXv3h1yuRzXr1/Hs2fPsrQOAHh7ewMAOnTogIEDB2Lbtm149+4dAKBjx47o2bOn9N1ARPmXn5+f9B3UsWNHFC5cGABgY2ODFStWICoqCpMnT1Z5QUhUVBSuXLkCAGjRooXG7WY0/unQoQPCwsIwfPhwqezz58+4e/cu/vjjD9y7dw9A5n7rUuvTpw/kcjn++OMPle2sXbsWQgh069ZNeqHP0aNHkZiYiEqVKqFUqVJq2zIxMYGPj0+Gjk1ZH8h6bHf+/HnExsaiRIkSaNq0qcY6vXr1ApDx+MbY2Bj29vYAci62a9q0KWQyGdavX48WLVpg8eLFuHXrFgDAxcUFgwYNQr169dLchjJG6Natm8blbm5uqFWrlsrvXEqajj27MW2XLl1gZGSE0NBQfP78WSrftGkTEhMTpb+FJpUrV8a6deuwcuVKlfKnT59i//79OH78OIDMX+vVq1fPVP3ixYtLL7/p378/jh07hsqVK2PhwoWZ2o4m2b1eM3ssqcXHx2Px4sXSf//++++IjIzUWj+92O7atWta47pTp05pXCfld2r37t0REBCApk2bwtraGidPnkSdOnVw+/ZtqX5W7mGysk6VKlVgb2+Pe/fuoXr16vjxxx9x9uxZfP78Gebm5ggODoafn5/Wc0X5138+wbV27Vp8+PABZmZm6NOnD5o3b67yiYuLAwAsWrRI4/rabjjLlCkDAHjy5AmA5B+Y0NBQ2NnZ4dy5cxg1ahQ8PDxQvHhx9O7dW+1LT7le+fLltbZduW9l3Zz2+vVr6Xx4eHhAJpOpfZRvynn27BkSExPT3J7yR/ft27fp1tUmO+dJGdSmpnxTobYvf10ICwuDu7s7Hj58iOnTp6Np06YoXLgw2rZti9WrV6d7PrJ7fdja2qqVyeVyKaDP6rErEwop36YYGhqKhIQE6YcnLXfu3MGoUaPQoEEDFC1aFAULFkTt2rURFBQk/Sgr/29GaDrO9BQqVEh6o6BCoUD//v21Bi2ZlZ2/W1aOJaWIiAiMHTsWcrkc/fr1AwCMGTMGZ8+e1VhfmYxUJoBSs7Ozg0juBSx9Hj16lGYbOnbsiHXr1kmfDRs24H//+x/u3r2LHTt2QKFQYMCAAdi7dy+A5O8cZeCr7bu2QIEC0o3LkydPsrQOAMyYMQMdOnRAXFwcQkJC0LFjRxQpUgT16tXDrFmz8Pr16zSPjYjyh3nz5knfQVu2bMHjx4/RuXNnvHv3DuPGjcODBw/U1nn48KH0/9vY2GiMf7p06aJWV5sPHz4gJCQEbdq0gZOTE8zNzVGhQgUEBATg0qVLADL3W5da6dKl0bJlS7x79w67du2SyteuXQsA6Nu3r1SmPN6LFy9qPC6ZTCa9WTIjx6aM7V68eJGltudEfAPkfGxXq1YthISEwMrKCuHh4Rg6dChcXFzg6OiIQYMG4fz58+luIzvHLpfLYW1trVY/u8ddrFgxNG/eHC9evFBJcK5btw6FChWCr69vmusnJSVh165d6Nq1KypXrgxLS0uUKlUKrVq1kt6sl9lrvUiRIpk+Dl9fX/Ts2RMKhQJAciJIF28eze71mpVjSSkoKAh//vknPDw84OnpiejoaAQGBmpNMKcX23377bdqsd3UqVPTbEPK79R169YhNDQUhw8fxtOnT/HVV1/h2rVraNGihfQWyqycs6ysY2Fhge3bt6Ns2bK4du0aJk6cCE9PTxQpUgSBgYHYunVrtr5nSX8Z53UD8lpISAiA5GBDU2Zd6fr16zh48KDakzttX47KfzCmpqZSWfv27fHw4UPs3bsX+/btw7Fjx3Dr1i2sWbMGa9asQb9+/aTeCxn5B6f8sUqrp096Uj6NyUxdbT0jUkpMTJSe5GlSpUoVWFpa4sOHDzh9+jQaNWqU5vaSkpIwduxY1KxZEy1btoSNjU22zlPqV+LmFE3n2MXFBZcvX8bx48exe/duHD16FH/99Rd2796N3bt3Y+HChThy5AgKFiyocZvZvT5y6th9fX1RsGBB7Ny5E+/fv4eVlRXWrl2LAgUKoEOHDmmuu2TJEgQFBSEpKQklSpRAvXr14OLiAg8PDzRp0gRlypTJdICW8gl8ZqS8Idi+fTumTJmikx5c2fm7ZfVYgORApnPnzlAoFBgzZgxmzJiBJ0+eYM+ePejcuTMuXryolvBNmay+desWnJ2ds7z/jGjfvj2+/vprLF68GAsXLsQXX3yR4cAj5TnLyjoAYGVlha1bt+L69evYuXMnDh06hFOnTuHMmTM4c+YMfvrpJxw5cgTu7u5ZODoi0leWlpZYu3YtHj9+jJMnT6Jly5b466+/UKxYMamO8nfcysoq3Sf+VlZWaS6/ffs2mjVrhsePH8Pc3Bw1a9ZEixYtUKlSJdSrVw9Lly7FqlWrsntY6Nu3L/bt24e1a9fC398fZ8+exc2bN1GtWjWVXiPKY3Nyckq3h1GtWrXS3W+dOnWwdOlSrb09Ujt37hwOHDiARo0aoVGjRnob36SkLXbu168fAgMDsWvXLuzfvx/Hjh3Dw4cPsWzZMixbtgw//vgjxo8fr3W7+hrT9ujRA/v378eGDRvg4+OD27dv4+zZs+jbt2+aSaJPnz6hVatWOHbsGGQyGdzd3eHv7w9XV1fUqlULDx48wIABAzLdnqzEQzExMVKPMQBYvnw56tatm+ntpJbd6zU7sd2qVavw66+/wtzcHKtXr4aVlRVq1KiBCxcuYNSoUZg/f77aOp6enjh27BiOHj2a5f1mlKWlJRYtWoTt27fjwYMH2LlzJwIDA7N0zrJ6nuvVq4dbt27h0KFD2Lt3L44ePYq///4bYWFhCAsLQ6tWrbBr1y4YGRll5RBJT/2nE1ynTp3CP//8A2trazx//lxroqhbt27YuHEjFixYoJbgevLkicYbX2X3UEdHR5VyCwsLdOzYER07dpTWX7lyJSZPnoxff/0VAwcORI0aNVCqVCncvXsXd+/e1Zqtvnv3LgCoBGGayGQyCCGkpxYpvX37Ns11U7Kzs4OZmRni4+OxcOFCrT2gMsrS0hKtWrXC1q1bsWnTpnQTXIcOHcLs2bMBAFevXoWNjY1Oz1NWKX+cNJ1fQPs5lslkUkAHALGxsfjf//4nPY359ddfMWLECI3rKocRKI9Nkzt37gDIuePWRHl9r169Gjt27EC9evVw6tQp9O7dG5aWllrXe/jwIYYNG4akpCQsXLgQQ4YMUQnWYmJicrRHXUrh4eH45ZdfUKhQITRu3Bj/+9//8PXXX2PHjh3Z3nZeXK8KhQKdO3fG8+fP4eXlJT2JW7VqFTw8PPDgwQN8+eWX2L59u8p6rq6uqFmzJv7880+EhIRgzpw5OmlPWqpUqQLg3+/PIkWKwNzcHJ8+fcK9e/c0Jtmio6Px8uVLAMnnrHDhwpleJyU3Nze4ublh9OjRUCgUiIiIwLfffouLFy9i/Pjx2Llzp06PmYjynomJCdavXw93d3c8ffoUvXv3Vhlqr/zNNTY2xtq1a7OVTBg6dCgeP34Mb29vhIWFqcVSM2fOzPK2U/L19UWRIkWwd+9eREdHY926dQBUe28B/x5blSpVpDrZ0b59exgbG+PevXs4d+4c6tSpk2b9+fPnY8OGDahduzbOnTunN/FNVmNna2traagWkNzWhQsXYsGCBfj+++/Rr18/qQdxaqVKlcKNGzdw9+5dadh8ankR23Xo0AFWVlbYtm0bQkJCpOskvZ758+bNw7Fjx+Dg4IC9e/eicuXKKsvTmiZB14YMGYL79+/Dx8cHf/75J1asWIE2bdpI92NZlVfX66VLlzB48GAAwC+//AIPDw8AwNKlS9GjRw8sWLAATZo0UXu43L17d8yaNQunT5/GlStXULVqVZ21SRNl79TXr19LsV1Wzll2zrOJiQlatWolTR/0+vVrbN68GSNHjsS+ffuwY8eObF8HpF/+00MUlb2lAgMD0+wF9fXXXwMA9uzZI82NoKQcSpNSTEwM9uzZAwDSvFrz589HuXLlMH36dJW6pUqVwoQJE6ReAcqu4s2aNQOgOtQrpX379uH169coUqQIqlWrluZxFihQAIDm7qgnT57UuI6m4M3Y2BheXl4AoHYzrLRmzRq4ubmpzWemzejRoyGXy7F8+XKtQ6WA5LkDRo8eDSB5nqJKlSoB0O15yqq0zu/ly5fV5j2IiIhA5cqV0aZNG5XyggULolu3blLAoGmYhFLjxo0hl8tx7tw5jV/2Qghs3LgRALQGSTlFGdRt2rRJ+rukNUcDAJw+fRoKhQJ2dnYYOnSo2vWX8t9ZykSXrp9Yvn79Gr1794YQAnPnzsXq1atRokQJ7Ny5UxqikR15cb1+9913OH78OOzs7LBp0yZpuELRokWxfv16yOVy7NixA/PmzVNbd86cOTAyMsIvv/yicrOnTUaGYKTl5s2bAP4d4m1sbIyGDRsC0H7ONm7cCCEE3N3dUbRo0SytEx0djTp16qBkyZJSF3rl/ps1a4ZJkyYBUP03mVs9QIkod5QpU0ZK5O/fv1+lF5WDgwPKly+P6OhorT0fpk2bBnd39zR76ADJMQAADB8+XC25FRcXhxMnTgDI/lA6U1NT9OjRAwkJCdi+fTtCQ0NhZmam1gNfGU8cPXpUmncwtfbt26N27drSkLK02NraYuDAgQCSk3kpv1NTO3r0qLRN5QO9WrVqoWDBgnj27JnWc62c5zEn45vMxs6jR49G6dKl1eagrFChAubPnw9ra2soFIo0pxVRxgjK+C2169ev4+LFizA2Nkbjxo0zfCzZZWVlBV9fX8TGxmLPnj3YsGEDypQpk24blNd6586d1ZJbwL+xXeprXde/r5s2bcK6detQpEgRrF69Wpp7q1+/fnj69Gm2tp0X1+u7d+/g7++Pjx8/omvXriq94Lp3744+ffoASE5mp56Py93dXbqv7dKlC6Kjo9PcV2JiojRsOisUCoV0n6KM7bJyD5OVdUJDQ+Hs7Cx9HykVKVIEgwcPluYWZGxngHJu/nr99vbtW+mNKidOnEizblJSkihfvrwAIIYPHy6E+PetNZaWliqvYX7//r3w9fUVAETLli2lcuXb0ezs7FReISuEEGfOnBFmZmbC2NhYPHjwQAghxNWrV4WpqamQyWRqr62/cuWKKF26tAAgJk+eLJVreyNio0aNBADh7++v8lagffv2CXNzc43rDBo0SOPbOFK+3nrXrl0qyy5cuCC9pWT27NlpntOUvvvuOwFAWFlZid9++03tTReRkZHSa61tbGxUXoGsy/OkpHz7UO/evVXKtb1FUfn2EltbW3Hv3j2pPCoqSnoFMlK84ejdu3fC3NxcyGQytdcTv3jxQnqlc8plym2kfJuP8m1/NWrUUHnzUWJiohg5cqQAIIoWLSqioqKkZem9HcTIyEgAml+jrommdn3+/FmUKFFCmJqaigoVKggHBweV607TWxQPHTqkdp6Ujh49Kr2VFIC4evWqtGzz5s3SOUgpvb+xtjfgdejQQQCQ3gokhBA7d+6U/q3fuHEjQ+dF21sUc+J6TUtYWJgAkl9DvW/fPo11lG8+NTExEWfPnlVbPnfuXOnvNXr0aPH06VO1OteuXRO9e/cWcrlcAMmvaU5Jed2lPK7Ujhw5In0nb968WSpXfueYmZmpve3myJEjwtraWu3fZVbW8fT0FABEUFCQytslExISREBAgAAg+vfvL5Vru/aISP+k9Ur71Jo2bSr9pqf8/fz1118FAOHo6KjyNjwhhNi7d6/0/RUaGiqVa3qLooODgwAg+vXrp/JW7ZcvX4ovvvhCaueoUaNU9qHc/pUrV1TKNb1FUeny5ctSmwHNb1AWQvXtwal/z6dMmSIACFNTU41vFNQkNjZWODk5CSD5Tbwp31yotG3bNlG4cGEBQLRv317lXIwfP14AEGXLllV523VSUpL0Vj8LCwuVZem90VoZxx85ckQqS+stipmNnZVv561QoYLa2yi3bdsmxbDv378XQmiOQ168eCFsbGwEADFu3DihUCikZQ8ePBDu7u5q8am2N1MraYtHtNEWH+3Zs0cAkN44PG7cOJXlmt6i2LNnTwFAVK1aVTpuIZJ/V5V/YwCidu3aKttS/jtYsGCBSnl6f2NNMe6DBw+kc5oytujYsaMAIJo3b672dntttN0H5MT1qk1SUpJo166dACBcXFxEbGysWp24uDjh6uoqnVtNbxCsU6eOACAcHBxEWFiYWp2EhASxbds26e2xAMR3330nLc/Id2piYqL45ptvpPuRlG3Nyj1MZte5fv26ACDMzc1V/t0LIcTt27ele9aUb/DWdu1R/vKfTXDNnz8/UzeNytfs2tjYiLi4OCmgaNCggZDL5aJhw4bC399f2NvbCwDC1dVV7Uawb9++0o+Ql5eX6NSpk7Q+oP5K6HXr1gkTExMBQDg7O4uAgADRuHFjKQnRtWtXlR8/bTfCe/bskfbh5OQk/P39Rc2aNaUfIk3rKM+PhYWF8PPzE8uXL5eWzZo1S8hkMgFAVK5cWXTs2FHUr19fKgsMDFRpV0YofxgBiCJFighvb28RGBgo6tatKx1v2bJlxcWLF9XW1dV5Uspsguv9+/eiQoUKUhKkbdu24osvvhAWFhaifPnyUnIuZfC5evVq6Xjd3d2Fv7+/aNmypbCyshIAROvWrVXarCmRFB0dLd2UW1hYCB8fH+Hv7y8lSezs7FSSr0LkToJLCCGGDx8uLUv9qnBNwZhCoZCSgUZGRqJp06bC399fCuZsbW1F8eLF1YLTS5cuSfvx9vYWwcHBQoisJbiUNy82NjbS67eVevXqJf2oJiQkpHte0goodX29anPjxg3p1dXjx4/XWk+hUAgvLy/p39jbt2/V6uzcuVM6/3K5XFSvXl34+fmJdu3aSa+cVgYRQ4YMUduG8rpzd3dXe+10YGCgqF69urSNL7/8Um3/Kb9zqlWrJgIDA6VrX9ONYFbWuXLliihUqJAAIEqXLi3atWsn2rVrJ0qWLCkAiHLlyql8p2u79ohI/2QmwXX79m0pmZQ6ITR48GABJD80qFGjhvD39xc1atSQtp36oaCmm9hFixZJ9V1cXIS/v79o1qyZMDMzU0kgpI5BlN+TyjhO+bAvrQSXEEKK9wBofdCRMp6wtLQUDRs2FB06dBBly5YVAISxsbHYsmVLmucttefPn6s85HN1dRW+vr7Cz89PajMA0aVLF/HhwweVdRMSEqSHxcbGxqJJkyYiICBAegBoZWUlwsLC0j3XKWU2wZXZ2DkxMVG0bNlSAMkPV5o1ayY6deokrSOXy8W6deuk+toSSeHh4dJvt4ODgxQfKpNqzZo1E3FxcVL93EpwJSYmSvc4ANQe+GlKcF24cEG6ru3t7YWvr69o06aNlFhQXutlypRR2ZYyhrSxsREdO3YU27dvF0JkPsH1+fNnKVEZEBCgUjcqKkpqx88//5yhc6PtPiAnrldtfvzxRynuT53sTunSpUvSuR82bJja8o8fP4rhw4dL17i1tbVo3LixCAgIEN7e3lLyWRkbpr5vSPmd6ufnpxLXdevWTbRv314UK1ZMig1Tf/dk5R4mK+v88MMPUjtr164tOnXqJJo2bSrF4QMGDFCpr+3ao/zlP5vgqly5sgAgvv/++wzVf/LkiXQDumTJEunH+fHjx2LChAnC0dFRmJqaCicnJzF27FgRHR2ttg2FQiGWLFki6tevL6ytrYWxsbEoVqyY8PX1FQcPHtS43ytXrohevXqJ0qVLCxMTE1GsWDHRunVrtV4JQqR9I3z06FHh4+MjrK2thYWFhahZs6ZYuXKl+Pjxo8Z1Pn78KPr27SsKFy4szMzMRM+ePVWWnzx5UgQEBIgSJUoIExMTUaJECdG0aVOxcePGTCe3lP766y8xcOBAUaVKFVGgQAFhZGQkbG1tRePGjcWCBQvUAqCcOE9CZD7BJUTyD+XQoUOFo6OjMDExEQ4ODuKbb74Rr1+/Fp07d9YYfO7bt0+0adNG2NvbCyMjI2FjYyMaNmwoQkJC1M6htkRSfHy8WLhwofD09BQFChQQFhYWwtXVVYwZM0Y8e/ZMrZ25leD6888/pWUpe1wJoT0Yi4mJERMnThRubm7CwsJCWFpaCjc3NzFixAjx9OlTMXToUI03EHPmzBEODg7CxMREODk5iaSkpEwnuG7duiUlF1evXq1W/+3bt1KiI+UTLG3SCyh1eb1qEhcXJwWOjRs3Tvff5MOHD4Wtra0UqGjb5oYNG0THjh2Fq6urKFiwoDA3NxcODg6ibdu2Yt68eeLly5ca11Ved5o+FhYWoly5ciIgIEDs3LlTaxtPnDgh/P39RbFixYSJiYkoWbKk6NSpkzh69KjO1rl+/bro3bu3KFu2rDA1NRWWlpbC3d1dTJw4UWPiT9O1R0T6JzMJLiGSE+TK+qm/l3bt2iXatm0rihYtKv3et27dWuzZs0dtO9puYrdv3y4aNmwo7OzspO8mPz8/cejQIXHhwgUBQJQoUUKlV8Vff/0l6tSpI8zMzIS1tbXUiz69BNeSJUukZEnKnkipxcfHi0WLFon69euLQoUKCXNzc1GxYkXRp08f8ffff6d7zjRJTEwUa9euFe3btxelS5cWpqamwtTUVJQpU0Z07dpVHDp0SOu6SUlJYs2aNaJJkybCxsZGmJmZifLly4uhQ4eK27dvq9XXdYJLiMzHzh8+fBAzZswQNWrUEAUKFBAmJiaiVKlSomvXruL8+fMqdbUlkoRIvl4HDx4snJychKmpqShSpIho3LixWLlypdrfMLcSXEIIERQUJCUKUtOU4BIiuRdhx44dRenSpYWxsbGwtbUVjRo1Er/99pv4+PGjlEhJmax59eqV6NixoyhYsKCwsLCQeotlNsE1ffp0KbmmKT5R9sQ2NTUVly5dSvfcpHUfoOvrVZPw8HApIfXrr7+mW3/x4sXS9a0pvhRCiLt374rp06cLLy8v4ejoKH2/VKpUSXz99ddi+/btGr83Un6npv7I5XJhbW0tqlevLkaMGCHu3r2rcd+ZvYfJ6jobNmwQzZo1E7a2tsLIyEgUKVJEtGzZUqW3rZK2a4/yF5kQfD8mERERERERERHlX//pSeaJiIiIiIiIiCj/Y4KLiIiIiIiIiIjyNSa4iIiIiIiIiIgoX2OCi4iIiIiIiIiI8jUmuIiIiIiIiIiIKF9jgouIiIiIiIiIiPI1JriIiIiIiIiIiChfM87rBhAREZHuyFqU1un2xMHHOt0eEREREWWdLmM9Q4vzDDbBpesAn+i/SPmF9+nzhzxuCVH+Zm5kmXs7k8lyb19EeYRxHlH2Mc4j0o1cjfMAxnpp4BBFIiIiIiIiIiLK1wy2BxcREdF/Eh9dERERERkuxnpaMcFFRERkSNhtnYiIiMhwMdbTirk/IiIiIiIiIiLK19iDi4iIyJDwoR4RERGR4WKspxUTXERERIaE3daJiIiIDBdjPa04RJGIiIiIiIiIiPI19uAiIiIyJHx0RURERGS4GOtpxQQXERGRIWG3dSIiIiLDxVhPK+b+iIiIiIiIiIgoX2MPLiIiIkPCh3pEREREhouxnlZMcBERERkSOaMeIiIiIoPFWE8rDlEkIiIiIiIiIqJ8jT24iIiIDAkf6hEREREZLsZ6WjHBRUREZEj4Zh0iIiIiw8VYTysOUSQiIiIiIiIionyNPbiIiIgMCR/qERERERkuxnpaMcFFRERkSPhmHSIiIiLDxVhPKw5RJCIiIiIiIiKifI09uIiIiAwJH+oRERERGS7GeloxwUVERGRI+GYdIiIiIsPFWE8rDlEkIiIiIiIiIqJ8jT24iIiIDAknHiUiIiIyXIz1tGKCi4iIyJAw5iEiIiIyXIz1tOIQRSIiIiIiIiIiytfYg4uIiMiQcOJRIiIiIsPFWE8rJriIiIgMCWMeIiIiIsPFWE8rDlEkIiIiIiIiIqJ8jQkuIiIiQyKX6fajQ/PmzYOLiwtiYmLSrXvv3j1Uq1YNvr6+GpcnJSVh06ZN8PPzQ/Xq1eHp6Yng4GBERkZqrB8fH4/ly5ejdevW8PDwgJeXFyZMmIAXL15k65iIiIiIcpWexnn6gAkuIiIiQyLT8UdHtm/fjuXLl2eorkKhwKhRo/Dx40etdSZNmoTJkyfj8+fP6NatGxo0aICDBw/C398fN27cUNve0KFDMWfOHFhbW6NXr16oWrUqwsLC4O/vj+fPn2fr2IiIiIhyjR7GefqCc3ARERFRjlEoFFiwYAGWL18OIUSG1lm0aBH++ecfrcsjIiIQFhYGLy8vhISEwNg4OZzx8/NDv379MG7cOGzdulWqHxYWhoiICPj7+2P69OlSeWhoKCZOnIhp06Zh4cKFWTxCIiIiItIH7MFFRERkSGQy3X6y4fTp02jXrh1CQkLg7u6OwoULp7vOxYsXsXz5cjRv3lxrnVWrVgEAhg0bJiW3AKBhw4Zo0qQJrl69ikuXLqnUl8vlGDFihMp2AgMD4ezsjPDwcERFRWXu4IiIiIjygp7EefqICS4iIiJDItfxJxt27NiBFy9eYOTIkdiwYQMsLS3TrP/+/Xt89913KFOmjFoySkmhUOD8+fOwtraGu7u72vIGDRoAAE6dOgUAePbsGe7fvw9nZ2fY2dlprJ+UlIQzZ85k9vCIiIiIcp+exHn6iEMUiYiIKEd06tQJY8aMgY2NTYbqz5gxA0+fPsWmTZtgZmamsc6TJ0+QkJAAFxcXyDQ8eXR0dASQPEk9AGnS+bJly2rcnoODg0p9IiIiIsqfmOAiIiIyJDrubu7t7Z3m8kOHDmldVqtWrQzv59ChQwgLC8PQoUPh7u6Ox48fa6z39u1bAIC1tbXG5YUKFQIAxMbGZqi+slxZn4iIiEivGeDQQl1hgouIiMiQ5MOY59WrV5gwYQKqVKmCQYMGpVlXoVAAAExMTDQuNzU1BQDEx8cDABITE1XK06tPREREpNfyYayXW5jgIiIiIq3S6qGlKxMmTMD79+8xe/ZslUnjNVEOXVQmrlJLSEgAAGm+L3Nzc5Xy9OoTERERUf7EBBcREZEhyWfd1jdt2oQjR45g7NixKF++fLr1lfN5aRtSGBMTA+DfoYrpDUGMjo5WqU9ERESk1/JZrJebmOAiIiIyJPnsjTi7d+8GkDzB/IwZM9SW37hxAy4uLihVqhQOHz6MUqVKwdzcHA8fPtS4PWV5hQoVAEBKmmmr/+jRI5X6RERERHotn8V6uYkJLiIiIsozHTp0QJ06ddTKY2JisGbNGtjZ2aFLly4oWLAgAEAul6NWrVo4ceIEbty4AVdXV5X1Tp48CQCoXbs2AMDe3h7lypXDjRs38ObNG9ja2qrVl8vlqFmzZk4cHhERERHlEia4iIiIDEk+67besWNHjeWPHz+WElxBQUEqywIDA3HixAnMmjULISEh0kTxx48fx9GjR1G1alV4eHio1J81axZmz56NGTNmQPb/5yg0NBS3bt3CF198AXt7+xw6QiIiIiIdymexXm5igouIiMiQ/AdiHh8fH/j4+GD//v3w9fVFs2bNEBUVhb1796JAgQKYOnWqSv2ePXviwIED2LZtG+7cuQNPT09ERkYiPDwcJUqUwJgxY/LoSIiIiIgy6T8Q62UVR28SERFRvjN37lyMGjUKMpkMa9aswZkzZ9CiRQts3rxZbdiiiYkJVqxYgYEDB+Ldu3dYtWoVrl27hoCAAGzevBnFixfPo6MgIiIiIl2RCSFEXjciJ8halM7rJhDle+LgYwDAp88f8rglRPmbuZFlru1LNrSKTrcnFv2j0+0R6QLjPKLsY5xHpBu5GecBuo31DC3O4xBFIiIiQ8J5GYiIiIgMF2M9rThEkYiIiIiIiIiI8jX24CIiIjIkfKhHREREZLgY62nFBBcREZEBkbHbOhEREZHBYqynHYcoEhERERERERFRvsYeXERERAaET/WIiIiIDBdjPe2Y4CIiIjIgjHmIiIiIDBdjPe04RJGIiIiIiIiIiPI19uAiIiIyIHI+1iMiIiIyWIz1tGOCi4iIyIBwXgYiIiIiw8VYTzsOUSQiIiIiIiIionyNPbiIiIgMCJ/qERERERkuxnraMcFFRERkQBj0EBERERkuxnracYgiERERERERERHla+zBRUREZED4UI+IiIjIcDHW044JLiIiIgPCbutEREREhouxnnYcokhERERERERERPkae3AREREZED7VIyIiIjJcjPW0Y4KLiIjIgMjAoIeIiIjIUDHW045DFImIiIiIiIiIKF9jDy4iIiIDwm7rRERERIaLsZ52THAREREZEMY8RERERIaLsZ52HKJIRERERERERET5GntwERERGRA5H+sRERERGSzGetoxwUVERGRAOC8DERERkeHSt1hv7969WLVqFe7cuQMjIyNUr14dQ4YMQdWqVTO0/qdPnxASEoI9e/bgyZMnsLCwQLVq1TBw4EDUrFkzU23hEEUiIiIiIiIiIsqUpUuXIjg4GK9evUJgYCBatGiBs2fPomvXrjh+/Hi66ysUCvTt2xdLliyBXC5H9+7d0bhxY5w+fRo9e/bEwYMHM9Ue9uAiIiIyIPr2VI+IiIiIdEdfYr07d+5gwYIFcHZ2xubNm2FpaQkA6NGjB7p27Yrx48fjwIEDMDc317qNnTt34s8//0SdOnWwYsUKmJiYAAACAgLQp08f/PDDD2jWrBmMjIwy1Cb24CIiIjIgMpluP0RERESkP/Qlzlu9ejWSkpIwePBgKbkFAG5ubujUqROioqJw6NChNLdx+fJlAIC/v7+U3AKAunXromLFinj58iUePnyY4TYxwUVERERERERERBl2+vRpAECDBg3UltWvXx8AcOrUqTS3YWNjAwB4/PixSnliYiLevHkDuVwu1ckIDlEkIiIyIPrSbZ2IiIiIdE+XsZ63t3eay7X1wEpMTMTjx49ha2uLQoUKqS13dHQEANy7dy/N7fv7+2Pt2rX4/fffUa5cOTRu3BjR0dGYM2cOXr58iS5duqBw4cIZPBomuIiIiAwKE1xEREREhksfYr13795BCAFra2uNy5VJr9jY2DS34+joiE2bNmHMmDEYMWKEyrKgoCAMHjw4U+1igouIiIiIiIiI6D8mvTmytFEoFACgMm9WSqampgCA+Pj4NLcTExODefPm4dq1a/Dw8ED16tXx5s0bHDp0CL///jvs7OzQpUuXDLeLCS4iIiIDog9P9YiIiIgoZ+hDrGdmZgYgeaiiJgkJCQCgMvm8Jt999x2OHDmCoUOHIigoSCp/+vQpunfvjsmTJ6Ns2bLw9PTMULs4yTwREZEBkclkOv3o0rx58+Di4oKYmBi1ZXFxcZg3bx6++OILuLu7o3r16ggMDERYWJjGbcXHx2P58uVo3bo1PDw84OXlhQkTJuDFixca68fExODnn39Gy5YtUbVqVTRt2hQzZ85Mt+s8ERERkT7RhzivYMGCMDIy0hpHKWM9TfNzKUVFReHIkSMoWbIkhgwZorKsZMmS0pDFzZs3Z7hdTHARERFRjtu+fTuWL1+ucVlMTAy6dOmCZcuWwdTUFF26dEHbtm3x+PFjTJgwAWPHjlWpr1AoMHToUMyZMwfW1tbo1asXqlatirCwMPj7++P58+cq9ePi4tCnTx/8+uuvcHR0RO/eveHo6IiVK1eia9euiIuLy7HjJiIiIjI0JiYmcHBwwOvXr/H+/Xu15Q8fPgQAVKhQQes2nj17BgBwcnKCXK6emnJxcQGQ3Jsro5jgIiIiMiAymW4/2aVQKDB37lyMGTMGSUlJGussXrwYt2/fRmBgILZt24bx48dj6tSp2LdvHypUqICtW7fi2LFjUv2wsDBERETA398fGzduxMiRI7FkyRJMnToVL168wLRp01S2v2zZMly9ehVBQUH47bffMHLkSKxevRoDBw7E7du3sWjRouwfKBEREVEu0Jc4r27duhBC4PTp02rLTp48CQCoXbu21vWLFi0KIPlNi0IIteWRkZEAAHt7+wy3iQkuIiIiA6JPQxRPnz6Ndu3aISQkBO7u7lpf87x7927IZDKMGjVK5QleoUKF0K9fPwBAeHi4VL5q1SrI5XK1t+0EBgbC2dkZ4eHhiIqKApA8B8SGDRtgbW2N/v37q9QfMmQIChcujD/++EOaK4KIiIhIn+lLnBcQEACZTIb58+erDFW8ceMGtmzZguLFi6N58+Za1y9VqhSqV6+Op0+fYtmyZSrL3rx5g3nz5gEAfH19M9wmTjJPREREOWLHjh148eIFRo4ciS+//BI+Pj54+/atSp3Pnz+jf//+eP/+vcZ5GpRv4VF2f3/27Bnu378PV1dX2NnZqdVv0KABbt26hTNnzsDX1xdXrlzB+/fv4e3tLW0r5bZr166NAwcO4MqVK6hVq5auDp2IiIjIoLm7u+PLL7/EihUr0K5dO7Rq1QpxcXHYtWsXFAoFpk+fLsVeMTExWL16NQCoTCY/c+ZM9OjRA7/88guOHj2KWrVq4c2bNzh8+DDevXuH7t27p5kkS40JLiIiIgOiD2/WUerUqRPGjBkDGxsbrXWMjIzQq1cvrcv37dsH4N95GJTd1cuWLauxvoODA4Dk7u6ZqR8ZGckEFxEREek9fYr1Ro8eDScnJ2zYsAEbNmyAlZUV6tSpg6FDh6Jq1apSvZiYGGlKiJQJrrJly2L79u0ICQnBkSNHsHr1apiamqJSpUro1q0bWrdunan2MMFFRERkQOQ6Dnq8vb3TXH7o0CGty7KbMAoPD8f+/fthaWmJDh06AIDUA8za2lrjOspyZVd5ZX1tSTZlfU1vdiQiIiLSN7qO9bIrICAAAQEBadYpXbo0bt68qXGZnZ0dxo8fj/Hjx2e7LZyDi4iIiPTOyZMnMXLkSADA5MmTpQlGExMTAUBtuKGSsjw+Pl6lvomJSYbqExEREVH+xB5cREREBkTXD/XS6qGVU3bs2IHx48cjMTER3377Lfz8/KRl5ubmAKB1UnhluaWlpUp9ZaIrvfpERERE+kzPOnDpFSa4iIiIDIg+zcuQWUIIzJ07F8uXL4eRkRF++OEHdOnSRaVO6iGIqUVHRwOANGF9ekMQU9cnIiIi0mf5OdbLaUxwERERUZ5LSEjAyJEjceDAAVhaWuKXX35B48aN1eqVL18eAPDw4UON23n06BEAoEKFClmqT0RERET5E+fgIp0xNTHF38vD0bhqPansl8E/QBx8rPIZ4ttHbd1OjdpAHHysUla6aAn8b+oqRG+/jsi1pzGsw1c5fQhE+UpU1AuMDP4WDT0bo3mTlvhp1s+cR4gg0/H/coNCocCQIUNw4MABFC9eHBs3btSY3AIAe3t7lCtXDjdu3MCbN2/Ulp88eRJyuRw1a9YEAFSuXBkFCxbEhQsX1IYpJiQk4Ny5c7CyskKlSpV0f2BEes7MxAy/jfgZb7ddxdNNf2JEp/5a67ao2QiXlh1A7M6bODhrI5xLO6ksH9lpAO6tOYW3265ixbdzYGX+77BfmwLWWDt6AV5v+QePN17A9L5jpB4IK0fNVYsVxcHHODR7s1obxnULwspRc3V09ETaxcfHY/KE7+FVtyG8G7XA6pVrtNa9fu0Gunfuibo16qFbYHdcu3pNZfne3XvRxqcd6taoh+CgEdLLT1ISQmDA14OwY9tOlfLIe5EY8PUg1K/thS9atMFvIb8jKSlJWn7q5GkEdAiEZ8366N93AO5H3s/wvl+/foNvg0ehQZ2GaNawOebNmQ+FQpHhfVPeyW9xXm5igot0wszEDBvHLUaVcq4q5ZUcK2LMbzNQPLC69Fmxb5NKHWurQlgweIraNkMnLEPcx/eoOaQ1hi2ZjGlfjoZfg1Y5ehxE+YUQAt8Gf4tPHz9h5doVmP3zTEQcicDiBUvyummUx2QymU4/uWHhwoWIiIhA8eLFsWnTJri6uqZZPzAwEAqFArNnz4YQQioPDQ3FrVu34OPjI01Kb2pqCl9fX7x+/RpLly5V2c7ixYvx7t07dO3aFcbG7NRO/z0/9Z+AWs5V0WxUZwxeOB6TewyHf8M2avUqlXHG7h9XY8fpA6g5+Av8dfsfHP4pVEpi9W/THd/3GoFxK2ehQXAHlLIrjg3jFknrL/lmGkoWKYaGIzqix8xv0KdlgPTgctjiySpxouc37fEp4RMWbF+h0oYuTX3xQ6+ROXg2iP4196d5uHb1Gn5duRzjJo1FyJLlOLj/oFq9Dx8+YujAINSoWR0bw9bDo5oHhg78Bh8+fAQA/H3lH3w/cQoGDu6PtRtXIzYmBhPHTVbZRlJSEmZOm4Uzp86olH/8+BFDBgbB3t4eG0LXYdyEMVi3dj1CN4UBAO7cvougQd+gSbMm2PTHBrhVcsPXX/bHh/cfMrTvcd+NQ2xcHNZsWIWf583Gvt37sOr31RnaN+Wt/Bbn5SZGc5Rtbo4VsWHcIo0ZYDfHivgpbBmi3r7Uuv5P/Sfg7rMHKFGkmFRmU8Aa9SrVRL953+HOk0jceRKJfReOwru6F7af3Jcjx0GUn9yPvI8rl//G4YhwFLErAgAYHDQIc36ahxGjhudx64gy7sWLF1ixIvlG1s3NDX/88YfGek5OTmjTJvnGu2fPnjhw4AC2bduGO3fuwNPTE5GRkQgPD0eJEiUwZswYlXW/+eYbnDhxAosXL8Zff/0Fd3d3XLlyBWfOnIGbmxsGDRqUswdJpIcszS3w9Rdd8cW4nrh45x9cvPMPZpdxxlDfPthyfLdK3UHteuHUtT8xefXPAIDRv01DW09vdPfugOW71yPI90vM+WM5Nh3ZAQDoPXs4nmy8AOfSTrj1+B5a12mG7jOCcO3BLVx7cAsbjmyHd3Uv/LL1N8R8iEXMh3/n1Fv93TyERezGjlP7AQBGciMsHDoVfVoG4u7TB7l0dui/7MOHj9i2ZTsWhyyCWyU3uFVyw907d7Fpw2a08GmhUnf/3v0wMzfDiFHDIZPJ8N3YUTgRcQIH9x+Eb4f22LRhE1q2aoF2vu0AANNm/ohWzVvj8eMnKF26FKKiXmDc6PF48ugxChYqqLLtvy78hZjoaEz8fjxMTU1RtlxZ9OzVHXt370WXbp0RujkMHtWqYkjQYABA8MhhiDgagd279iCgc6c0921vXxS2RYpg0JABcCzjCABo7uONi39dzNC+ifSVXvTgev36NZ4+fYp3797ldVMoCxpX9cSRS6dQb1h7lfKClgVQumgJ3Hp8T+u6jap6oolHPUzbsFCl/GP8J7z/+AFf+gTC2MgYzqWd0KByLVy880+OHANRflPEzg5Lli+WkltKcbFxedQi0hf5rQfX6dOnpTcZHjlyBIsWLdL42b373xtuExMTrFixAgMHDsS7d++watUqXLt2DQEBAdi8eTOKFy+usg9ra2ts3LgR3bt3R2RkJFauXIknT56gb9++WL16NQoUKJDjx/lfxjhPP3k4VYKJsTFOXbsglZ345zzqulZX+7fvVMIRZ69fVCn7O/IG6lWq+f/Ly+DsjX+XP3/zAi+jX0vLX8e8RQ/vjrAwM0eJIsXQqlYTjTFds+oN0MjdE+NWzJTKClhYoaqTG+oGtcPp639m/8CJ0nHr5k0oFApUq+YhlVWvUR1/X/lHbYje31f+RvUa1aR/MzKZDNVqVMPlS1cAAFcu/40aNWtI9YuXKI4SJYrj78vJy69fu47ixYth4x8bUDDVb5GLqwvmLZwHU1NTlfLY/4/1njx6DPeq7lK5TCZDBeeKuHI5/X2bmppixuxpUnLrzu27OHY4ArXq1MrQvilv5ac4L7flWQ+urVu3YtGiRTh79iw+ffoklVtYWKB27doIDg6Gr69vXjWPMmHZrrUay90cKyIpKQnju32DL2o3xeuYt5i7ZTnWHEx+Om9qYorlwbMwZOF4JKSaFyU+MR5DFo3HoqE/YliHr2BsZIyV+zerDW8k+q8qVKggGnjVl/47KSkJmzZsRl3POnnYKtIH+hyrHD58WK3M19c3S7/3lpaWGD58OIYPz1iPRVtbW0yaNAmTJk3K9L4o8xjn6b8StsXwKvoNEhX/xmBR717CwswcRQoVxqvof+e4i3r7EqXsVBPHDkVL4k3su3+XF/l3uaW5BWwL2sCukC0AYPCC8Vg7ej5id9yEkZERDv4ZgR/WzlNr05jOQ7DqQCgev3wmlUW/j4FXcAedHDNRRrx6+Qo2NjYwMTWRyooUsUV8fDzevXsHW1tbqfzly1coX0F1PjrbIkVw9/YdaVv29kXVlkc9fwEAaNK0MZo01TznpF1RO9gVtZP++9OnT9jyxzY0btJI2s6LqBcq60Q9f45C///24PT2rdS311f488JfqFTZDZ27ds7Qvilv6XOsl9fypAfX3Llz8eWXX8Lb2xt79uzB1atXcefOHVy9ehW7du1Cs2bN0Lt3byxcuDDdbcXHxyMmJkblw0mW9YOrQ3kIIXDj0R20Ht8Lv+3diOXBs6R5tCZ2H4a/7vyNg39GaFzfzbEi/nfmIDy/aY8+Pw1Hp4Zt0K0ZAxwiTeb9/AuuX7uBocFD87opRPQfxzgvf7A0t0B8YoJKWfz/96Y0MzFTKd989H8IaNQGbep6w0huhF4tOqG2iwdMTZITAJuP/Q9juw6Bq2MFmJmYYe6A5Hl+lMtdHMrjwq0raDC8Azp8/zWqlHXB6C6DVfZRrrgjmlVrgIXbV+bI8RJl1MdPn2CaIrkFQOrJlJig+lD+08dPMDUxTVXXROqZ/OnTJ5iYalie6t9eepKSkjBx3GR8eP8eX/XrCwBo9UVLHNh/EMeORkChUGDn9p24+s816YUqGd336HHf4bdVvyIhIRFjRqkO8de2byJ9lSc9uObMmYM1a9ZofHLn6uqKJk2awN3dHUFBQQgKCkpzWzNmzMAPP/ygUjZ58mQttSk3rTn4B/53Jhxv///p3t+R1+Fc2gmD2vXE7SeR6N+6O9z7N9e4brPqDfD1F11RumttfEr4hD9vXUEpu+KY0P0bbDi8LRePgkj/zZszH+vXbsDsOTNRsWKFvG4O5TFD7G5O+QvjvPzhU8InmKW6MTf7/5vhD/EfVcr3XziKH9bNw5ZJy2FsZIwjl09hzcEtsLZKnjNo6vpf4FTCEVd/PYxERSJCdq/HpbtXEfMhDhVKlcOcARPh0K0Onr9J7jliaWaBpd9Mx6xNS/A56TMAwL9ha1y6exXXH97O6UMnSpOZqRkSEtTfugsA5ubmqnXNTNUSRgkJiTC3SK5namaKxAQNy1NtJy0KhQITx05CxLEIhPy2VOpZ1aBhAwwcPAAjh32Lz58/o3adWmjbvg3i4uIytW8XVxcAwJRp36NbYA88efIUpUqVTHPflLcY62mXJz24Pnz4gLJly6ZZp3Tp0oiOjk53W2PHjkV0dLTKZ+zYsTpqKWWXMrmldP3hbZQqUhz+Xq1hW8gGd9ecROzOm9g7PXmYY+zOm+jWrANqVqyK208i8Snh32ENF+9cRRn70rnZfCK9N+PHmVi7ai2mzfoRzVtqThjTf0t+m4OLDA/jvPzhyavnsLO2hZHcSCorXtgeHz59xLs49b/N9A0LUcjPDSW61ECL0V1R0NIK96MeAQA+fPqIzj8OQuEOlVE0wAPDlkxCGfvSuP/8EaqXr4xX0W+k5BYAXLzzDwpZFYRtIRuprFXtJtj+/xPLE+Ul+2JF8e7dOygUCqns1avXMDc3V5sI3r6YPV6/eq1S9vrVK9jZJQ8NtLe3xysNy4tmMFGUmJiIUSNG48jho1i8bCGqVa+msrzfwK9x6vwJhB87iOUrQvDh/QeULFky3X3HxcVh3979KnOKOZVPHmr57u3bDO2b8g7jPO3yJMHVsWNH9OnTB8ePH1f54gCSu0CeOnUKffv2hb+/f7rbMjMzQ6FChVQ+ZmZm6a5HOe+H3t/i4KyNKmXVylfGjUd3sXDHSrj2bYJqA31QbaAPvp47Knn5QB/sPH0AT19HoULJsjAx/rd7sKtDeUQ+f5irx0Ckz5YtDsEfoVsw6+cZ+KJ1q7xuDhERAMZ5+cWlu1eRqFDA0+3fSai9qtTG+VuXIYRQqdulqS/mDfoeCYkJePnuNcxNzdHUoz6OXDoFAJj19Xj0atEJMR9iEfshDrWcPWBtVRCnrv2Jp6+jYGdti6I2/74UxdWxAmI/xOHlu39vvms7e+Dk1fM5fNRE6XNxdYGxsTGuXP5bKrv410VUrlIJcrnq7bN7VXdcuvjvvxkhBC79dRlVPZInf6/q4S69mRAAnj97jufPo+DuUTVDbZky+UecOXUGS5YvRq3atVSW7d29F7Nn/ARTU1MUKWKLT58+4fy586hdt3a6+/708RNGjxyDv6/8e4zXr16HkZERypQtk+6+ifRVniS4lixZAi8vL/j4+MDS0hIlS5ZEuXLlULJkSZibm6NFixZo0KABlixZkhfNIx353+mDaFzVEyM7DYBTiTIY2LYnerXwx89hy/A29h3uPr0vfZ68eg4AuPv0PuI+vsf/Th9EoiIRv434CRVLlUNbz+YY1zUIC7avyOOjItIP9+7ew/Jlv+LLr/ugeo3qePXylfSh/zb24KK8xjgvf/gY/wmrD4Zh2bAZqOXsAd/6Pvg2YADmb/sdAFCscFGYmyYPZbr1+B4Gtu2BDl5foEKpctgwdhEevXyKveePAACevn6OyT2Ho5azB2pUdMe6MQuwdNdavI19hzPX/8K1B7ex5rv5qFTGGY2qeuKnfuOxaMcqqS1lipVGIauCuPaAwxMp71lYWKCdb1v8+MM0/PP3VRwOP4I1K9eiW89uAJInb1e+PKOFT3PExsZi9oyfcPfOXcye8RM+fvyIlq1aAgACuwRg187d2LplG27dvIXxYyeiUZOGKF26VLrtOH3qDHZu34mR342Ao6ODFOe9eZP8AogyZcsgbPMfCD94CA/uP8CYUeNQrHhxeDVskO6+7YrawbtFM8z4cRauX7uBvy78he8nTUHX7l1QoECBdPdNeYtxnnYykfoRTS768OEDLl++jGfPnuHDhw8wNzdHqVKlUK1aNVhYWGRr27IWHMqWF8TBx2gyMgDHrpwGALSv1xJTen8L59JOuB/1CONXzsa2E3vV1mtctR6OzglT+bu5OVbE/ME/oI5rNbx89waLdqyUgi7KHeLgYwDAp88f8rgllNrvv67AgnmaJ2i+fO2ixnLKO+ZGlrm2L5d5uu3Nd3P4Pp1uj/47GOfpPwszcyz9Zgb8G7ZG9PtY/BS6VIq1xMHH6PPTcKw+EAYA6OMTiEndg1GkUGEcungSgxeOk4YdyuVyzBkwCT28OyIpKQlrD23B6F+nS/NrlbIrgfmDf0BTj/qI+/Qea8O34Ps1c6H4nNzDr45rdZxd+D+YtXZKc/LtlaPmAgC+/GlEjp2T/xLGedp9/PgR06ZMR/iBQyhQsAD69O2NHr26AwA8KlXHlGk/wLdDewDA31f+wY8/TEPkvUhUdK6ICZPHw62Sq7StHdt2YsnCpYiOjka9BvUwecpE2NjYqO3zi+atMXDIQGm7U7//EX+EblGrV7JkCewN3wMA2L51B0KWLkf0u2jU8ayD8ZPGomjRf9+cmNa+Y2Nj8dPMOTh25BgAoK1vGwQPHwYTU5MM7Zv+lZtxHqDbWM/Q4rw8TXDlJAY+RNnHwIdIN5jgItItxnlE2cc4j0g3mODSH3nyFkUiIiLKGYbY3ZyIiIiIkjHW044JLiIiIgPCoIeIiIjIcDHW0y5PJpknIiIiIiIiIiLSFfbgIiIiMiB8qkdERERkuBjraccEFxERkQFhzENERERkuBjracchikRERERERERElK+xBxcREZEBYbd1IiIiIsPFWE879uAiIiIiIiIiIqJ8jT24iIiIDAif6hEREREZLsZ62jHBRUREZEAY9BAREREZLsZ62nGIIhERERERERER5WvswUVERGRA+FCPiIiIyHAx1tOOCS4iIiIDwm7rRERERIaLsZ52HKJIRERERERERET5GntwERERGRI+1SMiIiIyXIz1tGKCi4iIyICw2zoRERGR4WKspx2HKBIRERERERERUb7GHlxEREQGhA/1iIiIiAwXYz3tmOAiIiIyIOy2TkRERGS4GOtpxyGKRERERERERESUr7EHFxERkQHhUz0iIiIiw8VYTzsmuIiIiAwIgx4iIiIiw8VYTzsOUSQiIiIiIiIionyNPbiIiIgMCB/qERERERkuxnraMcFFRERkQNhtnYiIiMhwMdbTjkMUiYiIiIiIiIgoX2MPLiIiIgPCp3pEREREhouxnnbswUVERGRAZDKZTj+6NG/ePLi4uCAmJkbj8r1796Jz586oWbMm6tSpgwEDBuDKlSsa6yYlJWHTpk3w8/ND9erV4enpieDgYERGRmqsHx8fj+XLl6N169bw8PCAl5cXJkyYgBcvXujs+IiIiIhymr7GefqACS4iIiLKcdu3b8fy5cu1Ll+6dCmCg4Px6tUrBAYGokWLFjh79iy6du2K48ePq9WfNGkSJk+ejM+fP6Nbt25o0KABDh48CH9/f9y4cUOlrkKhwNChQzFnzhxYW1ujV69eqFq1KsLCwuDv74/nz5/r/HiJiIiIKHdxiCIREZEB0bencQqFAgsWLMDy5cshhNBY586dO1iwYAGcnZ2xefNmWFpaAgB69OiBrl27Yvz48Thw4ADMzc0BABEREQgLC4OXlxdCQkJgbJwczvj5+aFfv34YN24ctm7dKm0/LCwMERER8Pf3x/Tp06Xy0NBQTJw4EdOmTcPChQtz6hQQERER6Yy+xXr6hD24iIiIDIhMpttPdpw+fRrt2rVDSEgI3N3dUbhwYY31Vq9ejaSkJAwePFhKbgGAm5sbOnXqhKioKBw6dEgqX7VqFQBg2LBhUnILABo2bIgmTZrg6tWruHTpkkp9uVyOESNGqOw3MDAQzs7OCA8PR1RUVPYOloiIiCgX6Eucp4+Y4CIiIqIcsWPHDrx48QIjR47Ehg0bVJJXKZ0+fRoA0KBBA7Vl9evXBwCcOnUKQHKPsPPnz8Pa2hru7u5q9ZXbUNZ/9uwZ7t+/D2dnZ9jZ2Wmsn5SUhDNnzmThCImIiIhIX3CIIhERkQHRp27rnTp1wpgxY2BjY6O1TmJiIh4/fgxbW1sUKlRIbbmjoyMA4N69ewCAJ0+eICEhAS4uLhqPNXV95aTzZcuW1bh/BwcHlfpERERE+kyfYj19wwQXERGRAdF10OPt7Z3m8pRDB1OrVatWutt/9+4dhBCwtrbWuFyZ9IqNjQUAvH37FgB0Vl9ZrqxPREREpM+Y4NKOQxSJiIgozygUCgCAiYmJxuWmpqYAgPj4+CzVT0xMVClPrz4RERER5U/swUVERGRAdP1UL60eWrpgZmYG4N9EVGoJCQkAIM3fldn6yjcvKsvTq09ERESkz9iDSzsmuIiIiAxIfot5ChYsCCMjI61DBGNiYgD8O/RQOZ9XRuunNwQxOjpapT4RERGRPstvsV5u4hBFIiIiyjMmJiZwcHDA69ev8f79e7XlDx8+BABUqFABAFCqVCmYm5tL5enVL1++vEp5ao8ePVKpT0RERET5ExNcREREBkQmk+n0kxvq1q0LIQROnz6ttuzkyZMAgNq1awMA5HI5atWqhbdv3+LGjRvp1re3t0e5cuVw48YNvHnzRmN9uVyOmjVr6ux4iIiIiHJKfovzchMTXERERIZEJtPtJxcEBARAJpNh/vz5KkMJb9y4gS1btqB48eJo3ry5VB4YGAgAmDVrlsrcWsePH8fRo0dRtWpVeHh4qNRXKBSYPXs2hBBSeWhoKG7dugUfHx/Y29vn5CESERER6UY+i/NyE+fgIiIiojzl7u6OL7/8EitWrEC7du3QqlUrxMXFYdeuXVAoFJg+fbrKWxB9fHzg4+OD/fv3w9fXF82aNUNUVBT27t2LAgUKYOrUqSrb79mzJw4cOIBt27bhzp078PT0RGRkJMLDw1GiRAmMGTMmtw+ZiIiIiHSMCS4iIiIDkl+7m48ePRpOTk7YsGEDNmzYACsrK9SpUwdDhw5F1apV1erPnTsXq1atwtatW7FmzRpYW1ujRYsWCAoKkubdUjIxMcGKFSsQEhKC3bt3Y9WqVShatCgCAgIQFBSEYsWK5dZhEhEREWVLfo31coNMpOyrb0BkLUrndROI8j1x8DEA4NPnD3ncEqL8zdzIMtf25f1HL51u71CnNTrdHpEuMM4jyj7GeUS6kZtxHqDbWM/Q4jzOwUVERERERERERPkahygSEREZEHZbJyIiIjJcjPW0Y4KLiIjIgMgZ9BAREREZLH2L9fbu3YtVq1bhzp07MDIyQvXq1TFkyBCNc6hqEx4ejtWrV+PatWsAgLJly6JHjx7w9fWFXJ7xgYccokhERERERERERJmydOlSBAcH49WrVwgMDESLFi1w9uxZdO3aFcePH8/QNubNm4chQ4YgMjISvr6+aN++PaKiojBmzBj89NNPmWoPe3AREREZEHZbJyIiIjJc+hLr3blzBwsWLICzszM2b94MS8vkyfZ79OiBrl27Yvz48Thw4ADMzc21buPMmTNYtmwZKlWqhJUrV8LGxgYAEBwcjI4dO2LFihXo0qULypQpk6E2sQcXERGRAZHr+ENERERE+kNf4rzVq1cjKSkJgwcPlpJbAODm5oZOnTohKioKhw4dSnMbv//+OwBgxowZUnILAKytrTFy5EgEBAQgOjo6w21i7EpERERERERERBl2+vRpAECDBg3UltWvXx8AcOrUKa3rx8fH49SpU6hQoQJcXV3Vlrdu3Ro//vhjpuby4hBFIiIiA6JvE48SERERke7oQ6yXmJiIx48fw9bWFoUKFVJb7ujoCAC4d++e1m3cvn0bCoUCLi4uePLkCRYuXIjjx48jNjYW5cuXR58+feDr65updjHBRUREZED0ZV4GIiIiItI9XcZ63t7eaS7XNsTw3bt3EELA2tpa43Jl0is2NlbrtqOiogAAL1++RIcOHWBjY4NWrVohLi4O4eHh+O6773Dv3j0MHz48I4cCgAkuIiIiIiIiIiLKIIVCAQAwMTHRuNzU1BRA8jBEbd6/fw8AOHfuHJo0aYIFCxbAzMwMAPDo0SMEBARg2bJlaNasGTw8PDLULia4iIiIDIg+dFsnIiIiopyhy1gvvUngtVEmohITEzUuT0hIAACVyedTMzIykv7/H374QdomADg4OKBfv36YPXs2du7cyQQXERHRfxGHKBIREREZLn2I9QoWLAgjIyOtQxBjYmIAQOP8XEoFChQAANjb26N48eJqy6tUqQIAePDgQYbbxbcoEhERERERERFRhpiYmMDBwQGvX7+Whhqm9PDhQwBAhQoVtG7DyckJwL+9vVJTDoO0sLDIcLuY4CIiIjIgch1/iIiIiEh/6EucV7duXQghcPr0abVlJ0+eBADUrl1b6/oODg4oXbo03r17h3/++Udt+eXLlwEAbm5uGW4TY1ciIiIDIpfJdPohIiIiIv2hL3FeQEAAZDIZ5s+frzJU8caNG9iyZQuKFy+O5s2bp7mNnj17AgB+/PFHlZ5gkZGRWLlyJczNzeHn55fhNnEOLiIiIiIiIiIiyjB3d3d8+eWXWLFiBdq1a4dWrVohLi4Ou3btgkKhwPTp06W3KcbExGD16tUAgKCgIGkbvXr1wvnz5xEeHo42bdqgRYsWiIuLw4EDB/DhwwdMmTIFJUuWzHCbmOAiIiIyIPow8SgRERER5Qx9ivVGjx4NJycnbNiwARs2bICVlRXq1KmDoUOHomrVqlK9mJgYLFq0CIBqgksul2PBggUICwuTPiYmJvDw8EC/fv1Qr169TLWHCS4iIiIDwmGFRERERIZL32K9gIAABAQEpFmndOnSuHnzpsZlRkZG6NKlC7p06ZLttnAOLiIiIiIiIiIiytcy1INrypQpGd7gpEmTstwYIiIiyh79eqZH+QHjPCIiovyDsZ52GUpwHTlyJEMbk8lkDHyIiIjykL51Wyf9xziPiIgo/2Csp51OE1xERERElL8wziMiIiJDkKU5uO7du4dRo0bBz88Pz549w4oVK3Dy5Eldt42IiIgySS6T6fRD/z2M84iIiPQX4zztMp3gioiIQNWqVREZGYl9+/bh48ePuHHjBpo2bYqtW7fmRBuJiIgog2QymU4/9N/COI+IiEi/Mc7TLtMJru+++w4zZ87EH3/8ARMTEwDA7NmzMXv2bM7LQERERJSPMc4jIiKi/CrTCa6///4brVu3Vitv37497t69q5NGERERUdZwiCJlB+M8IiIi/cY4T7tMJ7jKli2L8+fPq5Xv3r0bZcuW1UWbiIiIKItkOv7QfwvjPCIiIv3GOE+7DL1FMaUff/wRffr0wYULF6BQKLBmzRpERkZi06ZNWLt2bU60kYiIiIhyAeM8IiIiyq8y3YOrQ4cOiIiIQFRUFKpUqYIdO3YgPj4eERERCAwMzIk2EhERUQZxiCJlB+M8IiIi/cY4T7tM9+ACAA8PD6xZs0bXbSEiIqJsMsRghXIX4zwiIiL9xVhPu0z34AKANWvWoH79+ihcuDCKFSuGxo0bY8eOHbpuGxERERHlMsZ5RERElB9lOsE1ceJEBAcHo1WrVli7di1+/fVXeHl5oWfPnvjll19yoIlERESUUTKZTKcf+m9hnEdERKTfGOdpl+khiiEhIVizZg3atm0rlbVv3x7VqlXDsGHDEBwcrMv2ERERUSaw2zplB+M8IiIi/cZYT7tM9+ASQsDR0VGt3NnZGR8/ftRJo4iIiIgo9zHOIyIiovwq0wmuyZMnY8CAAbhx44ZU9ujRIwQHB2P8+PE6bRwRERFljkzHn+z6/PkzVq1ahXbt2sHd3R3Vq1dHjx49cPjwYbW6SUlJ2LRpE/z8/FC9enV4enoiODgYkZGRGrcdHx+P5cuXo3Xr1vDw8ICXlxcmTJiAFy9e6KDl/02M84iIiPSbPsV5+kYmhBDpVZLL5SrjM4UQkMlksLKyglwuR2xsLGQyGWxtbfUmqJS1KJ3XTSDK98TBxwCAT58/5HFLiPI3cyPLXNvXoCPDdbq9pU3nZWv94cOHY8+ePShRogSaN2+OT58+Yffu3fjw4QPGjx+PXr16SXUnTJiAsLAwODs7o1GjRnj+/Dn27dsHMzMzbNiwAa6urlJdhUKBQYMGISIiAjVq1ECtWrVw9+5dHDp0CPb29ggLC0Px4sWz1fb/CsZ5RP9NjPOIdCM34zxAt7FeduM8fZOhObiOHDmS0+0gIiIiA3P27Fns2bMHTk5OCAsLQ4ECBQAAX375JTp16oSff/4Z7dq1Q+HChREREYGwsDB4eXkhJCQExsbJIYqfnx/69euHcePGYevWrdK2w8LCEBERAX9/f0yfPl0qDw0NxcSJEzFt2jQsXLgwdw84n2KcR0RERIYgQwmuxo0bZ2hjz549y1ZjiIiIKHv0aeLRy5cvAwDatGkjJbcAoHz58vD09MThw4dx9epVeHl5YdWqVQCAYcOGScktAGjYsCGaNGmCI0eO4NKlS6hWrRoAYNWqVZDL5RgxYoTKPgMDA7F27VqEh4cjKioKxYoVy9mDNACM84iIiPIPfYr19E2m5+C6efMm/Pz8ULFiRTg5OcHJyQnlypVDyZIl4eDgkBNtJCIiogzS5aujs/v66MKFCwMAnjx5orYsKioKAGBrawuFQoHz58/D2toa7u7uanUbNGgAADh16hSA5ETL/fv34ezsDDs7O431k5KScObMmWy1/7+IcR4REZF+05c4Tx9lOsHVr18/vHz5EqNGjcLz588xcuRIBAQEIDo6Gr///ntOtJGIiIjyIR8fH9jb22PHjh1Yv349oqOj8fLlS0yfPh1Xr15Fw4YNUalSJTx58gQJCQlwdHTUGGwp3+p37949AJAmnS9btqzG/SoTMcr6lHGM84iIiCi/ytAQxZTOnz+P06dPo1q1alizZg3c3NwwZMgQODs74/fff0fv3r1zop1ERESUAZl+cpUOb2/vNJcfOnRI67JChQph06ZNmDBhAqZMmYIpU6ZIyzp37iy9le/t27cAAGtra63bAYDY2NgM1VeWK+tTxjHOIyIi0m+6jvUMSabPjYmJCWxsbAAArq6uuHjxIgCgRYsWuHLlik4bR0RERJmjT0MUExISsHjxYpw9exYVK1ZEz5494e/vjyJFimDr1q0ICQkBkPxGRCA5xtDE1NQUABAfHw8ASExMVClPrz5lHOM8IiIi/aYvcZ4+ynQPrvr16+Onn37Czz//jFq1amHjxo0YMWIELly4AHNz85xoIxEREeWRtHpopWfWrFnYsmUL/P39MWXKFGny+Ldv3+Krr77C4sWLUapUKTg7OwP4N3GVWkJCAgDA0jL5NdzKeENZnl59yjjGeURERJRfZboH19y5c7F//34sWbIEPXv2xIsXL2Bra4suXbpgyJAhOdFGIiIiyiC5TKbTT1YlJSUhLCwMpqammDBhgsqbEQsXLowJEyYAADZv3iz1GNI2pDAmJgbAv0MV0xuCGB0drVKfMo5xHhERkX7ThzhPX2W6B1elSpVw+/ZtfPz4EZaWlrhw4QKOHj2KIkWKwNPTMyfaSERERBmkL8HK69evER8fj7Jly2rsSeXi4gIAePr0KUqVKgVzc3M8fPhQ47aU5RUqVAAAlC9fXqU8tUePHqnUp4xjnEdERKTf9CXW00cZSnBpCyCVlK/0fvjwofSmIyIiIvrvsra2hqmpKZ4/f47379/DyspKZbnyTYj29vaQy+WoVasWTpw4gRs3bsDV1VWl7smTJwEAtWvXltYpV64cbty4gTdv3sDW1latvlwuR82aNXPq8AwK4zwiIiIyBBlKcJUtW1ZlAjIhhNqEZMqyz58/67aFWSQOPs7rJhAZDHMjzmNDlF/oy4ShpqamaNmyJXbt2oVZs2bh+++/h1yePDPC+/fvMXPmTABA+/btAQCBgYE4ceIEZs2ahZCQEGmi+OPHj+Po0aOoWrUqPDw8pO0HBgZi1qxZmD17NmbMmCEdd2hoKG7duoUvvvgC9vb2uXnI+RbjPKL/NsZ5RPmLvsR6+ihDCS7lU1YiIiLSb3LoT9Azfvx4XLt2DZs3b8bFixfRoEEDfPjwAceOHcPz58/RrFkz9OjRAwDg4+MDHx8f7N+/H76+vmjWrBmioqKwd+9eFChQAFOnTlXZds+ePXHgwAFs27YNd+7cgaenJyIjIxEeHo4SJUpgzJgxeXHI+RLjPCIiovxDn2I9fSMTQoi8bkRO+Pj5fV43gSjfszBKHlIUmxidxy0hyt8Kmljn2r6+O6nbxM7sBjOztX5cXBx+//137N+/H48ePYKRkREqVqyIjh07onPnzlKvLgBQKBRYtWoVtm7dikePHsHa2hq1atVCUFCQNO9WSh8+fEBISAh2796N58+fo2jRomjQoAGCgoJQrFixbLWb9Nut6H/yuglE+Z6zdRUAgPW4unncEqL8LXr62Vzdny5jvezGefqGCS4i0ooJLiLdyM0E1+hTY3W6vVn1Z+h0e0S6wAQXUfYxwUWkG7md4NJlrGdocV6m36JIRERE+otv1iEiIiIyXIz1tJOnX4WIiIiIiIiIiEh/ZSnB9fnzZ+zevRvz5s3Du3fvcPbsWURHcwgTERFRXpPp+H/038M4j4iISH8xztMu00MUHz16BB8fH7x+/Rpv3ryBr68vZs+ejVOnTuHAgQNwd3fPiXYSERFRBvDV0ZQdjPOIiIj0G2M97TLdg2vo0KHw8vLC06dPYW5uDgDYtGkTWrRogW+++UbnDSQiIiKi3ME4j4iIiPKrTPfgioiIwNmzZ2FkZCSVmZiYYOLEiahRo4ZOG0dERESZw4lHKTsY5xEREek3xnraZboHl6WlJaKiotTKb968iUKFCumkUURERJQ1Msh1+qH/FsZ5RERE+o1xnnaZPqKBAwdiwIAB2L17N4QQuHXrFlauXIl+/frhq6++yok2EhEREVEuYJxHRERE+VWmhyhOnDgRNjY2GDRoED58+IDWrVvD3t4eI0aMwLfffpsTbSQiIqIMYrd1yg7GeURERPqNsZ52mU5wAUBQUBCCgoLw/v17KBQKWFtb67pdRERElAV8sw5lF+M8IiIi/cVYT7tMJ7jWrFmT5vJevXpluTFERERElHcY5xEREVF+lekE1+TJk1X+W6FQ4MWLFzA2NkbdunUZ+BAREeUhGfhUj7KOcR4REZF+Y6ynXaYTXJGRkWplcXFxGDBgANzd3XXSKCIiIsoazstA2cE4j4iISL8x1tNOJ++FLFCgAL7//nvMnTtXF5sjIiIiIj3BOI+IiIjygyxNMq/J5cuX8fnzZ11tjoiIiLKAE49STmCcR0REpB8Y62mX6QRX06ZN1U5obGwsLl++jBEjRuisYURERJR5ct10zqb/KMZ5RERE+o2xnnaZTnA1adJE5b9lMhlMTU0xc+ZMeHt766pdRERERJTLGOcRERFRfpXpBNfr168xbNgwlC9fPifaQ0RERNnAbuuUHYzziIiI9BtjPe0y3bdt3bp1MDIyyom2EBERUTbJZDKdfui/hXEeERGRfmOcp12me3CNGDECgwcPxvDhw1GmTBmYm5urLHd0dNRZ44iIiIgo9zDOIyIiovwq0wmuSZMmAQD27dsH4N/ucUIIyGQyvmGHiIgoD8lheE/jKPcwziMiItJvjPW0y1CCKyIiAvXr14exsTEiIyNzuk1ERESURYbY3ZxyFuM8IiKi/IOxnnYZSnA1bdoUz549g729PcqUKZPTbSIiIiKiXMI4j4iIiAxBhhJcQoicbgcRERHpgJxP9SiTGOcRERHlH4z1tMvwHFzsBkdERKT/ZJyXgbKAcR4REVH+wFhPuwwnuGrVqpWh10bfu3cvWw0iIiIiotzFOI+IiIjyuwwnuEaOHAlra+ucbAsRERFlk1wmz+smUD7EOI+IiCh/YKynXYYSXDKZDF26dIG9vX1Ot4eIiIiygUPNKLMY5xEREeUfjPW0y1Dqj5OPEhERERkmxnlERERkCDKU4OrduzcsLCxyui1ERESUTTId/48MH+M8IiKi/EPf4ry9e/eic+fOqFmzJurUqYMBAwbgypUrWd7euXPn4ObmhsGDB2d63QwNUVy5cmWmN0xERES5j6+OpsxinEdERJR/6FOst3TpUvzyyy8oXbo0AgMDERMTg927d+PEiRNYtmwZGjZsmKntxcbGYvTo0UhKSspSezI8yTwREREREREREdGdO3ewYMECODs7Y/PmzbC0tAQA9OjRA127dsX48eNx4MABmJubZ3ibU6ZMwdOnT7PcJk6/T0REZEA4RJGIiIjIcOlLnLd69WokJSVh8ODBUnILANzc3NCpUydERUXh0KFDGd7enj17sHPnTjRv3jzLbWKCi4iIyIDIZTKdfoiIiIhIf+hLnHf69GkAQIMGDdSW1a9fHwBw6tSpDG0rKioKP/zwA+rWrYuePXtmuU0cokhEREQ56vz58/j1119x6dIlJCYmwsHBAR07dkS3bt1gamoq1UtKSkJoaCg2bdqEBw8ewMzMDJ6enhg2bBjKlSuntt34+HisXr0a27dvx5MnT1CwYEE0adIE33zzDezt7XPzEImIiIj+MxITE/H48WPY2tqiUKFCassdHR0BAPfu3Ut3W0IIjB07FgqFAjNnzsSjR4+y3C4muIiIiAyITKZfnbNDQ0MxadIkFChQAK1atYKFhQWOHj2KGTNm4NKlS5g3bx5k//8EcdKkSQgLC4OzszO6deuG58+fY9++fYiIiMCGDRvg6uoqbVehUGDo0KGIiIhAjRo14O3tjbt37yIsLAzHjh1DWFgYihcvnleHTURERJQjdBnreXt7p7lc2xDDd+/eQQgBa2trjcuVSa/Y2Nh027B27VqcPHkSM2bMQMmSJZngIiIiomT6NG/WvXv3MHXqVJQoUQJr165F6dKlAQDffvstevXqhb179yIwMBD169dHREQEwsLC4OXlhZCQEBgbJ4cofn5+6NevH8aNG4etW7dK2w4LC0NERAT8/f0xffp0qTw0NBQTJ07EtGnTsHDhwtw9YCIiIqIcpg+xnkKhAACYmJhoXK7soR8fH5/mdu7cuYOff/4ZzZs3R8eOHbPdLia4iIiIKEesWbMGCQkJGD9+vJTcAgAzMzMMHz4cO3fulAKkVatWAQCGDRsmJbcAoGHDhmjSpAmOHDmCS5cuoVq1alJ9uVyOESNGqOwzMDAQa9euRXh4OKKiolCsWLGcPUgiIiKifCozk8CnZGZmBiB5qKImCQkJAKAy+XxqiYmJGDVqFKysrDBlypQstSM1/RrHQERERNmiT5PMHz58WJoXKzVPT09Mnz4djRo1gkKhwPnz52FtbQ13d3e1usrJS5UTlT579gz379+Hs7Mz7OzsNNZPSkrCmTNnstV+IiIiIn2jD3FewYIFYWRkpHUIYkxMDABonJ9LacGCBbh27RqmTp2KIkWKZLktKbEHFxERkQGRZTMppStv375FVFQUqlWrhpiYGCxevBjh4eF48+YNHBwcEBgYiF69ekEul+PJkydISEiAi4uLxvannqg0MjISAFC2bFmN+3ZwcFCpT0RERGQo9CHWMzExgYODAx48eID379/DyspKZfnDhw8BABUqVNC6jd27dwMAhgwZonH5oUOH4OLigjp16mDt2rUZahcTXERERKRVVicfjYqKApDcRb1Tp05ITExE06ZNIYTA4cOHMWPGDPz999+YM2cO3r59CwAZnqg0vfrK8oxMbEpEREREmVe3bl3cv38fp0+fRvPmzVWWnTx5EgBQu3Ztrev36tVLY6z25MkTbNu2DeXKlUObNm1QqlSpDLeJCS4iIiIDIteDiUcB4P379wCAa9euoUqVKlixYoWUeAoODkb37t2xa9cueHt7w97eHkDGJypVzvegLE+vPhEREZGh0JdYLyAgAKGhoZg/fz7q1q2LggULAgBu3LiBLVu2oHjx4mqJr5T69Omjsfzs2bPYtm0bnJycEBQUlKk2McFFRERkQHTdbT2rk48aGRlJ//+4ceNUelsVKVIEwcHBGDZsGHbu3Cl1Tc/oRKXm5uYq5enVJyIiIjIU+jBEEQDc3d3x5ZdfYsWKFWjXrh1atWqFuLg47Nq1CwqFAtOnT5ceOsbExGD16tUAkOmkVWYwwUVEREQ6p3yKJ5PJNE4cX6VKFQDAgwcPYGNjA0D7kMLUE5WmNwQxOjpapT4RERER6d7o0aPh5OSEDRs2YMOGDbCyskKdOnUwdOhQVK1aVaoXExODRYsWAWCCi4iIiDJIJtOPFyQ7ODjAxMQEiYmJSExMVBtOqFAoAAAWFhYoVaoUzM3NpQlJU0s9UWn58uVVylN79OiRSn0iIiIiQ6EvsZ5SQEAAAgIC0qxTunRp3Lx5M0Pbq1u3bobrpqZfZ4aIiIiyRQ6ZTj9ZZWpqiurVqwMATpw4obb88uXLAAA3NzfI5XLUqlULb9++xY0bN9Tqpp6o1N7eHuXKlcONGzfw5s0bjfXlcjlq1qyZ5fYTERER6SN9iPP0FRNcRERElCN69eoFAJgzZw5evXollb98+RKLFi2CTCZDYGAgAEj/d9asWSpzax0/fhxHjx5F1apV4eHhIZUHBgZCoVBg9uzZEEJI5aGhobh16xZ8fHykyeuJiIiIyPBxiCIREZEB0ZeJRwGgRYsW6NmzJ9auXYs2bdqgVatWAIDw8HC8evUKgwcPlpJWPj4+8PHxwf79++Hr64tmzZohKioKe/fuRYECBTB16lSVbffs2RMHDhzAtm3bcOfOHXh6eiIyMhLh4eEoUaIExowZk+vHS0RERJTT9CnW0zcykfKxpwH5+Pl9XjeBKN+zMLICAMQmRudxS4jyt4Im1ulX0pH1t1fqdHvdK36Z7W3s2bMH69evx7Vr1yCTyeDi4oLevXtLCS8lhUKBVatWYevWrXj06BGsra1Rq1YtBAUFSfNupfThwweEhIRg9+7deP78OYoWLYoGDRogKCgIxYoVy3a7SX/div4nr5tAlO85Wye/7MN6XN08bglR/hY9/Wyu7k+XsZ4u4jx9wgQXEWnFBBeRbvzXE1xEusYEF1H2McFFpBtMcOkPDlEkIiIyIOy2TkRERGS4GOtpxwQXERGRATHEN+IQERERUTLGetrxLYpERERERERERJSvsQcXERGRAZHJ+OyKiIiIyFAx1tOOCS4iIiIDImO3dSIiIiKDxVhPO6b+iIiIiIiIiIgoX2MPLiIiIgPCN+sQERERGS7GetoxwUVERGRA2G2diIiIyHAx1tOOQxSJiIiIiIiIiChfYw8uIiIiA8Ju60RERESGi7GedkxwERERGRA5u60TERERGSzGetpxiCIREREREREREeVr7MFFRERkQNhtnYiIiMhwMdbTjgkuIiIiAyJj52wiIiIig8VYTzueGSIiIiIiIiIiytfYg4uIiMiAsNs6ERERkeFirKcdE1xEREQGRMY36xAREREZLMZ62nGIIhERERERERER5WvswUVERGRA5Oy2TkRERGSwGOtpxwQXERGRAWG3dSIiIiLDxVhPOw5RJCIiIiIiIiKifI09uIiIiAwI36xDREREZLgY62nHBBcREZEBkbFzNhEREZHBYqynHc8MERERERERERHla+zBRUREZEDYbZ2IiIjIcDHW044JLiIiIgMi55t1iIiIiAwWYz3tOESRiIiIiIiIiIjyNfbgIiIiMiDstk5ERERkuBjraccEFxERkQGRsds6ERERkcFirKcdhygSEREREREREVG+xh5cREREBoTd1omIiIgMF2M97ZjgIiIiMiAyds4mIiIiMliM9bTjmSEiIiIiIiIionyNPbiIiIgMiJzd1omIiIgMFmM97diDi4iIyIDIdPw/Xbp37x6qVasGX19ftWVJSUnYtGkT/Pz8UL16dXh6eiI4OBiRkZEatxUfH4/ly5ejdevW8PDwgJeXFyZMmIAXL17otM1ERERE+kRf4zx9wAQXERER5TiFQoFRo0bh48ePGpdPmjQJkydPxufPn9GtWzc0aNAABw8ehL+/P27cuKG2raFDh2LOnDmwtrZGr169ULVqVYSFhcHf3x/Pnz/PjUMiIiIiIj3CIYpEREQGRF/frLNo0SL8888/GpdFREQgLCwMXl5eCAkJgbFxcnji5+eHfv36Ydy4cdi6datUPywsDBEREfD398f06dOl8tDQUEycOBHTpk3DwoULc/aAiIiIiPKAvsZ6+oA9uCjXJCQkwL99AM6fuyCVXbl8Bb269UG9mg3g27oDtv6xLQ9bSKTfjoQfQa0qdVQ+3w0fAwAYEfSt2rLjR4/ncYspL+jjEMWLFy9i+fLlaN68ucblq1atAgAMGzZMSm4BQMOGDdGkSRNcvXoVly5dUqkvl8sxYsQIle0EBgbC2dkZ4eHhiIqK0knbiQxJQnwCFkxdjC7NeqLXF19h2/qd6a5z9dJ1fO03SKWsXR1/jZ/Du48CAN69icaMMT+hS7Oe6N9xCMJ3Hda47aePnsG/YVe18kvnLmNIl2D4N+yK8YMm4/kTzb0yt67djq98B6Z7DES6ZmZsikUdx+PBxHDcHLMbQ726aa3btlJjnAvehCeTj2Bf/+XwKOkiLTM1MsHUVkG4Nvp/eDDxINZ1n4WShewzvJ96Zavh2JDVePr9URwfuhZNytdWWR7k1R1Xvt2GBxPDsdh/IqxMLVSWj/Xuh9vj9uL+hAOY7zcWZsam2TktlEv0Lc7TJ0xwUa6Ij4/HmG/H4u6du1LZq5evMGRAEGrVrolNWzZi0NCBmDVtNiKO8aacSJN7dyPRsElD7Du6R/pM/GE8ACDybiSmzpyisqxu/bp53GIi4P379/juu+9QpkwZtYQUkDzc8Pz587C2toa7u7va8gYNGgAATp06BQB49uwZ7t+/D2dnZ9jZ2Wmsn5SUhDNnzuj4SIjyv5UL1uD29bv4cckPGPRdf2z8LRQnD53WWv/+nQeYOeZnCCFUytfs+U3l49/TD/YliqJu49oQQmD6d7Pw+sVrTFv6A74e/iV+/2U1Th1R/Tf5MuoVpoyYjoT4BJXyF89fYtqo2WjerhnmrpqFQoULYdqoWWpteP7kOTb8GprNM0KUNVO/CEK1Um5o//sQjNw5G6ObfQ3fKs3U6rnal8Nvnadg7rHV8FrYA38/u4XQXnNhYWIGABjXvB/aVm6MfqGT0DKkP0yMjLGu+8wM7cfOqjA29fwZW64cRP353bD973Bs6PmTlCD7sk4HjPH+Gj8cWAqfkH4oWagofus8Rdr28Ea98LWnP77aNAEdVwWjUfmaGNPs65w8bUQ5jgkuynF379xDzy698fjRY5XyI4eOws6uCL4ZHoQyZR3RqrUP2rZvg7279uVRS4n02/1791GhQnnY2dlJn4KFCiIhIQFPnzxFpSpuKstMTfkU7r9IJpPp9JNdM2bMwNOnTzF79myYmZmpLX/y5AkSEhLg6OiocX+Ojo4AkieoByBNOl+2bFmN+3NwcFCpT0TJPn38hAM7D6H/yL6o4OqEek3rwr+HH3aF7dVYf+/WA/ju63GwsbVWW1bYrrD0iY9PwP9C9yBo/CBYFbDCnet3cf3KTXw7NRjlXZxQp2Et+Pf0w9a1O6T1Tx89i+G9RsHExERt2wd2hKOCW3l06N4eZco7YtikoYh69hL//HVVpd7imcvh5FIum2eFKPMsTczRq1Z7jNk1F5ef3sSua8cw//ha9PPspFa3WcW6uBEViU0X9yLyzRP8sH8Jiheyg6t98rXbrUZbTD2wDCcjL+Lmi0h8s206ajpUhlMRh3T341mmKj4nfcaC4+tw/+1TzDm2GvGKBNR2rAIA6F8vAItObMCWKwdw40UkBv7xA1q5eKGCnSPkMjmGeHXFhD0LEHHvT/z1+Bqmh/+KaqVcc+9EUpbpU5ynb5jgohz354U/UbtuLazesEqlvH7D+vhh2vdq9ePiYnOnYUT5zL17kXAs66hW/iDyASADSpUulQetIn0j1/H/vL290/yk5dChQwgLC8PAgQM19s4CgLdv3wIArK3Vb6IBoFChQgCA2NjYDNVXlivrE1GyyNv3oVAo4Fr13+FRlaq54tbV20hKSlKr/+epvxA8OQi+3dqmud31yzehai13VKvjAQB4/iQK1oULoXip4lKdshXL4M71u1AoFACACyf/QvcBXdF/ZF+17d385zYqV3eT/tvc3AzlXZxw4+9bUtnh3UcR/ykeLdun/R1ElBOqlKgIE7kxzj68IpWduX8ZtRwqqyUM3nyIhmuxcqjrWBUymQzda7ZF9Kc4RL5+AplM9n/t3Xlc1VX+x/E3IIsgXETELdcQsdTMtMz6maWjjS2OpqhNilmZhFjjklYWmqNOTpmllku5tGipo0k5jg4uleSSSy4oIWW4pBaKICiX7fv7g/HWFa6gXbmLr6eP+3h4v+d8z/dzeXDhw+eec74asjReG9O2l7qGyS+g3OucOZ+lGgHBeujmTpKkB5p3VDUffyWfTJMkNapeTzuO/lYYPnXutDLOn9XtDVqqea0mquEfrNUHvrS0L9uzVj0XDLfL1wjXlj3zPHfDJvO45qL69SnzeL16dVWvXl3L8zOnz2jtmnV6OnZIZYUGuAzDMJT+U7q2JG3VgnkLVFRUrC7dOmvosKd1+MefVK1aNb3ywnjt/HanatWupadjh+iu/+vg6LBxHcvIyNC4cePUokULxcTE2Ox38Q/esmZySLLMRDSbzZKkgoICq+Pl9QdQ4kxGpoJMQVbvteCQYOWb83Uu65xM1a2LxuNeL9nj0db+WVLJcsKv1m7W1Pcm/TZmjWDlnjuvvDyz/PxKZm1mnMpQUVGRcnPOyxQcpLiXSn4m7NtZ+sYTmRmZqhEaYnUsOMSkjF9OS5KyMrO0cOaHmjgzXocOpl3JlwCwi9qBoTp9PksFRYWWY7/knFFVbz+F+Jt0Oves5fiKvYnq3ryj1g2dp8KiQhUbhqI+GKGzeSUfwmz64VursWM69FVGbqb2n0zTnyP/77LX+ean7zR3yzJ90H+Kio1iVfGqopjlryot44gk6decM6obVNNyrr+3n6pXDVIN/2A1ql5XmReydXvDVorvGqMQ/2AlJG9U/H9mKr+o4Fp82YBK4fIFLrPZXCqJ9fX1dYNXdn3Jy8vTyGdHqUZoDfWOesTR4QBO5+SJk8q7kCcfH29NeWOKfj52XK9PeUPmPLOCTEHKy8vTnXe116AnBmrj+k0aMWykFnz8vm5qcZOjQ0cls/d08/Xr11/VeePGjVNubq6mTp1qtXH8pS4uW7xYuLpUfn7J/jz+/v6SJD8/P6vj5fWHa7OZ5+GKmfPM8vaxfi9efF6QX1jWKeX6b8J6hTe/Uc1aRFiONbu5qUJqVtfcf76nIaOe0JmMTH22+HNJUmFB+dcpidO64O3t7a2C/JKfEe+9uVCdH7xXDW9sQIELDlHV20/mQuvfQfn/e+7rZf3hS4i/SWHVamhkwj+148h+PXFHL8165GV1nDlQGbmZVn27N++ouLv/qr+tek0FRYXlXqeaj78ahdTTlA3vaW3KZj10cye99uBIfXt0vw79mq4V+/6rEZ2itSV9j9Izf9bkB56TJPl4VVGAr7+qevtpfLdYvbj6TXl5eunNHmPk5emp5z9/w55fLlwD7ri00F5cfk7alClTZDKZrB5TpkxxdFi4Audzz2t4zLNKTz+it995S1WrVi3/JOA6U6duHa1P+q/i//6KmkVG6N4u92rE2BFaufwzPfH0YK1Zv1oP/eVBRURG6OnYIepw951aufwzR4cNB3CGuyh+8skn2rhxo0aMGKEbb7zxsn2Dg4Ml2V5SmJ2dLem3pYrlLUHMysqy6g/XRp5nPz4+PqUKWRef+/pd3Z6NSeu3qNP9Ha2v4+ujMVNGac+O/ep77wCNHTJO9/fsKknyDyg/x/P2+a2YZYmzoEC+fr7atWW3UvZ9r35PlL06AKgM5kJzqbsN+vzv+YWCPKvjE+4fpgOn0vTe1uX67ucUDf9sis7nX9Bjt1kv/X2geUct7D9Jc7Ys0wc7Eip0nWc7DpCHpKkb3teen7/X3/87RzuP7ldMh76SpKkb5mv38YPa9uwSHX1lvcyF+dp3IlXZ5lwVFRfJ38dPY754Q1/9uFMb07brpX+/pYFtH6Z44gIcnec5M4fNc/rqq68q3Ldjx44221544YVSd2Xy9fVVsa7ukyhUrpycHMU+HaejR45q7vw5aljG/kIASly651DjJo1kNpuVnZWt6iHVrdoaNWmkH9MOV2Z4gMXq1asllRQnyipGpKSkqFmzZqpXr54SExPl5+enI0eOlDnWxePh4eGSZCmY2ep/9OhRq/5wjGud56XnHbrq2K5XNcJClJ2VraLCInlV8ZIkZZ7OlI+vjwICA654vF9PZejo4WO64552pdoibgrX+6veVWZGpoKCg7R723cKCg5SVf/yC1w1wmoo8/RZq2OZp8+qSURjffXfJGWcOq3HupXs3VVUVKTCgkL1ueevGj/9Jd18K7OWce39nP2ravib5OXppaLiIklSrcAaOp+fZ1l6eFHrepGa882nlueGYWj/yUOqH/zbHnWPtPqT5vQZr/nbV+jFf0+v8HVa14vU/pPWPwv3nkhV81olvyfPF+Rp0JKXFOQbIEPSOXOu0l5coyOZJ5STf16SlPpruuXcQxlHVNXbT6EB1fVrzpk//oUCHMBhBa7Y2FgdOHBAkkrd9vf3PDw8VFRUZLPd19e3zKnqF4oocDm74uJijRw+SsePHtf7i+apcRPuhAPYsiVpi8Y9/7JWJ34hv6olS7RSU1JlCjbprTfeloeHp+L//rKlf2rKIYVHXH7mDNyTM3zy2rNnT91+++2ljmdnZ+uDDz5QaGio+vXrp8DAQHl6eqpt27bavHmzUlJSFBlpfQenpKQkSVK7diV/RIeFhalx48ZKSUnRmTNnFBISUqq/p6enbrvttmv06lAR1zrPU17pQ7i8xhGNVaVKFaXsT9XNrUs2cT+wJ0VNbwqXp+eVL+pI3X9IobVCFVa7ptXxc1nnNHHUPzTun2NVPbTkw5dvk3apZZubKzRusxZNdWDPQcvzvDyzfkw9rEefitKfHu6sqMd/28piy8Zt+nzpak1+91XVqBlS1nCA3e07kaqC4kK1q99CW9P3SJLaN7xFu44fKPXz7mT2r2oWZv03TnhoQ+06VvI9fs+NbTWnz3jN3bLMqrhVkeuUNXbTmg2VnvmzJOnV+4fp4KkftWT3vyVJbeo1V5BfNW0/sk+FxUUyF+arZe2m2pC2TZLULKyRsvNydeZ8lh2+SriWnCHXc1YOW6K4Y8cO9ejRQ61atdL58+dVXFxc5uNySQ9c28p/faZvt+/QKxNfVmBgoDJ+zVDGrxnKOssPVeBSrVq3kq+fnybG/10/HU5X0tff6K03Zmjg4AHq2Kmj1nyxRl+sWq2jR45q3rvv6bvd36nvo1GODhsO4AxLFHv16qW4uLhSj+joaElSaGio4uLiNGjQIElSVFTJ9+prr71mtbfW119/rU2bNqlVq1a65ZZbLMejoqJUWFioqVOnWv0xsXTpUqWmpqpbt24KCwu7qthhH+R5zsfPz1f3de+kd/4xR6kH0rRl0zat/ChBD/d7QFLJ5u7mvIrfnCH9hyNq0PiGUscDTYHKO5+nhTM+0MnjJ7X2s0Qlfr5BvQb+pULj/umhzjq493stW7RC6T8c0VuvzlStOmFqeVsLBYeYVLd+HcvDFBIkLy8v1a1fR75+7M2GynGhwKwlu/6tN/8yRm3qNdcDzTsq7v/+qtn/m6kVVi1EflVKvh8XfbtK0e16qG/rP6tJyA0a3+0Z1Q+urcW7VsvL00sze41T0uFdmv7VBwqrFmJ5eHtVKfc6i3YkqGtEBz1zVz81ql5XMR36qUvTO/Xe1uWSpBPZGRrb+Um1qddcretGam7UBL2/bYUyL2TrnDlXi75dpakPjVTb+i3Urn4LTeg2TB/sWGWZLQbn5eg8z5k5bAaXr6+vlixZovbt22vcuHF6/fXXHRUKHGT9f9eruLhYw2OetTp+W7vb9P6ieQ6KCnBOAQEBmjHnLb3xjzc1sG+0/AP81atPTw18fIA8PDw0dtzzmj93vk6eOKUm4U00Y/Zbqvu7u5QCzqxbt27q1q2b1q5dqx49eui+++7TqVOntGbNGlWrVk0TJ0606j9gwACtW7dOK1euVFpamtq3b6/Dhw8rMTFRderU0dixYx30SnAReZ5zeuJvg/TuP+bqpZh4+Vfz16ND+qrDve0lSQO7P6lnX4lVlwfvq9BYZ8+cVbWgspc2Pj95hGZNmaNh/UeoVt0wjZk8UhE3VWzZcK26YXrhtdF6b9oCffLeMjVv1Uwv/XMMMxbgVF7893RN6zFGnz/5jrLzcjQlcZ4+T94kSTr04hrFLH9Vi3et1op9iQrwqaqRnaJV1xSmfScO6eH3Y5WRm6m29VuoQfU6alC9jg69uMZq/AfmxWjz4V2Xvc6Oo/v12Mdj9WKXIXqpy9NKyziiPov+ppRfSraomLNlqRpUr6Plg6ar2CjWJ7vXKH7tLKvX8Or9cVoePU0eHh769Lv/aMLadyrl6wdcKx7G5eaNV4KDBw/qyy+/1NChQ+067oWiXLuOB1yPqnqVJK7nCphVB/wRgd6m8jvZyY5fk+w6Xtuad9ltrGPHjqlz586KjIzUqlWrrNoKCwu1cOFCrVixQkePHpXJZFLbtm0VFxdX5kb158+f15w5c7R69WqdPHlSNWvW1F133aW4uDjVqlXLbjHjj7lWeV5q1n67jgdcjyJMLSRJphfvcHAkgGvLmrytUq9nz1zPnnmeM3B4getaocAF/HEUuAD7qNQCV8Y3dh2vbWgHu44H2AMFLuCPo8AF2EelF7jsmOu5W57nsD24AAAAAAAAAHtw2B5cAADA/txxw1AAAACUINezjQIXAABuhI2YAQAA3Be5nm0sUQQAAAAAAIBLYwYXAABuhGnrAAAA7otczzZmcAEAAAAAAMClMYMLAAA3wqd6AAAA7svZcr01a9Zo4cKFSktLk5eXl2699VbFxsaqVatWFTo/IyNDs2fP1saNG3Xq1Cn5+vrqpptuUnR0tLp06XJFsTCDCwAAN+Lh4WHXBwAAAJyHM+V57777rp577jllZGQoKipKf/rTn7Rt2zb1799fX3/9dbnnHz9+XD179tSHH36osLAwPfbYY+ratauSk5MVGxurmTNnXlE8zOACAAAAAABAhaWlpentt99WRESEPv30U/n7+0uSHnvsMfXv318vvfSS1q1bJz8/P5tjTJkyRb/88ouGDx+u2NhYy/Hhw4erd+/emjVrlrp27aqIiIgKxcQMLgAA3IiHnf8BAADAeThLnrdo0SIVFxfrmWeesRS3JKl58+bq3bu3Tp06pfXr19s8//z589q4caOCg4M1dOhQq7Y6deqof//+Ki4u1oYNGyocEwUuAADcCAUuAAAA9+Used6WLVskSXfddVeptg4dOkiSvvnmG5vnFxUVafTo0Ro+fLi8vLxKtfv6+kqScnNzKxwTSxQBAAAAAABQIQUFBTp27JhCQkIUFBRUqr1BgwaSpB9//NHmGIGBgRo0aFCZbYZhaO3atZKkZs2aVTguClwAALgRNoYHAABwX/bM9Tp37nzZdltLDM+ePSvDMGQymcpsv1j0Onfu3FXF9eGHH2rfvn2qXbv2Fd1JkQIXAABuhGWFAAAA7ssZcr3CwkJJkre3d5ntPj4+kiSz2XzFY69cuVJTpkyRl5eXXnvttctuUn8pClwAAAAAAADXmcttAn85F/fHKigoKLM9Pz9fkqw2n6+IuXPnatq0afL09NRrr72m9u3bX9H5FLgAAHAjLFEEAABwX86Q6wUGBsrLy8vmEsTs7GxJKnN/rrLk5+fr5Zdf1meffSY/Pz9Nmzat3OWTZaHABQCAG3GGaesAAAC4Npwh1/P29lb9+vWVnp6u3NxcBQQEWLUfOXJEkhQeHl7uWFlZWYqJidHOnTsVGhqqd999V61atbqquDyv6iwAAAAAAABcl+644w4ZhqEtW7aUaktKSpIktWvX7rJj5OTk6PHHH9fOnTsVERGhZcuWXXVxS6LABQCAW/Gw8z8AAAA4D2fJ8/r06SMPDw+99dZbVksVU1JS9K9//atCd0CMj49XcnKyIiMj9fHHH6tu3bp/KCaWKAIA4EacYV8GAAAAXBvOkuu1bNlSjz/+uObPn6+HHnpI999/v3JycvTFF1+osLBQkydPttxNMTs7W4sWLZIkxcXFSZKSk5P1xRdfSJKaNWtmab/ULbfcoo4dO1YoJgpcAAAAAAAAuCJjxoxRkyZNtHjxYi1evFgBAQG6/fbbNWzYMKulhtnZ2Zo5c6ak3wpcX375paV91apVNq8xcODAChe4PAzDMK7mhTi7C0W5jg4BcHlVvUo2CzxXkOXgSADXFuhtqrRrpWbtt+t4EaYWdh0PsAd7f58D16OLP99NL97h4EgA15Y1eVulXs+evwPdLc9jBhcAAG6EfbMAAADcF7mebWwyDwAAAAAAAJfGDC4AANyIs2w8CgAAAPsj17ONAhcAAG6FpAcAAMB9kevZwhJFAAAAAAAAuDRmcAEA4EaYtg4AAOC+yPVso8AFAIAb4c46AAAA7otczzaWKAIAAAAAAMClMYMLAAA3wqd6AAAA7otczzYKXAAAuBH2ZQAAAHBf5Hq2sUQRAAAAAAAALo0ZXAAAuBGmrQMAALgvcj3bKHABAOBGnCnpycnJ0bx587Ru3TodO3ZMVapUUdOmTdWnTx/16dPHqq/ZbNaiRYv02Wef6fjx4woMDFSnTp00fPhwhYWFlRo7Oztbc+fO1bp163Ty5EnVqFFD3bp1U2xsrAIDAyvrJQIAAFQqZ8r1nA1LFAEAgN1lZ2erX79+mj17tnx8fNSvXz89+OCDOnbsmMaNG6cXXnjB0rewsFDDhg3TG2+8IZPJpIEDB6pVq1ZatmyZHnnkEZ08edJq7JycHA0aNEjz5s1TgwYNFB0drQYNGmjBggXq37+/cnJyKvvlAgAAwMGYwQUAgBtxlo1HZ82apUOHDikqKkoTJkyQp2fJZ2qjR49W//79tWLFCt1///265557tGzZMn311Vd65JFHNHnyZMsYS5cu1csvv6xJkyZpxowZluOzZ89WcnKy4uLiNGzYMMvxN998U7Nnz9bMmTM1duzYynuxAAAAlcRZcj1nxAwuAADciIed/12t1atXy8PDQ6NHj7YUtyQpKChITz31lCQpMTFRkrRw4UJ5enpqxIgRVmNERUUpIiJCiYmJOnXqlCQpPz9fixcvlslk0pAhQ6z6x8bGqnr16lq+fLny8/OvOnYAAABn5Qx5nrOiwAUAAOyqqKhIQ4YM0bPPPqugoKBS7T4+PpKk3NxcnThxQj/99JMiIiIUGhpaqu9dd92l4uJibd26VZK0d+9e5ebmqm3btpZxfj9uu3btdO7cOe3du/cavDIAAAA4K5YoAgDgRuw9bb1z586XbV+/fn2pY15eXho4cKDNc/7zn/9Ikpo1a6bDhw9Lkho1alRm3/r160uSfvzxR0mqcP/Dhw+rbdu2l40dAADA1bBE0TZmcAEA4EacZYmiLYmJiVq7dq38/f3Vs2dPZWZmSpJMJlOZ/S8eP3funCRZ+gcHB1+2f3Z2tj3DBgAAcArOnOc5GjO4AACATWXN0LpaSUlJGjlypCQpPj5eYWFhKigokKRSyw0vunjcbDZLkqW/t7d3hfoDAADg+sAMLgAA3IqHnR/2sWrVKj399NPKy8vTqFGj9Je//EWS5OfnJ0k2N4W/eNzf39+q/8VCV3n9AQAA3Ivz5XnOghlcAAC4EWdLVQzD0LRp0zR37lx5eXlpwoQJ6tevn6X90iWIl8rKypIky2b15S1BvLQ/AACAO3G2XM+ZUOACAADXRH5+vkaOHKl169bJ399f06dP1z333GPV58Ybb5QkHTlypMwxjh49KkkKDw+/qv4AAAC4PlDgAgDAjTjLnXUKCwsVGxurr776SrVr19acOXMUGRlZql9YWJgaN26slJQUnTlzRiEhIVbtSUlJ8vT01G233SZJuvnmmxUYGKgdO3aooKDAai+u/Px8bd++XQEBAbrpppuu7QsEAABwAGfJ9ZwRe3ABAOBWnGMPrhkzZliKW5988kmZxa2LoqKiVFhYqKlTp8owDMvxpUuXKjU1Vd26dVNYWJikkk3ke/ToodOnT+vdd9+1GmfWrFk6e/as+vfvrypV+AwPAAC4I8fnec6K7A8AANjVL7/8ovnz50uSmjdvruXLl5fZr0mTJnrggQc0YMAArVu3TitXrlRaWprat2+vw4cPKzExUXXq1NHYsWOtzhs+fLg2b96sWbNmadeuXWrZsqX27t2rrVu3qnnz5oqJibnmrxEAAADOhQIXAABuxBk+i9uyZYvlboYbN27Uxo0by+zXuXNnPfDAA/L29tb8+fM1Z84crV69WgsXLlTNmjXVp08fxcXFqVatWlbnmUwmLVmyRDNnztT69eu1Y8cO1a5dW4MHD9bQoUNVrVq1a/4aAQAAHMEZcj1n5WH8fi2AG7lQlOvoEACXV9UrQJJ0riDLwZEAri3Q21Rp1zp14bhdx6tVtZ5dxwPsITVrv6NDAFxehKmFJMn04h0OjgRwbVmTt1Xq9eyZ67lbnsceXAAAAAAAAHBpLFEEAMCNcGcdAAAA90WuZxszuAAAAAAAAODSKHABAAAAAADApbFEEQAAN+LBvXUAAADcFrmebRS4AABwIyQ9AAAA7otczzaWKAIAAAAAAMClUeACAAAAAACAS2OJIgAAboRbRwMAALgvcj3bmMEFAAAAAAAAl0aBCwAAAAAAAC6NJYoAALgR7qwDAADgvsj1bGMGFwAAAAAAAFwaM7gAAHArfKoHAADgvsj1bKHABQCAGyHlAQAAcF/keraxRBEAAAAAAAAujRlcAAC4EQ8PPtcDAABwV+R6tlHgAgDArZD0AAAAuC9yPVtYoggAAAAAAACXxgwuAADcCJ/pAQAAuC9yPdsocAEA4FZIewAAANwXuZ4tLFEEAAAAAACAS2MGFwAAboQ76wAAALgvcj3bmMEFAAAAAAAAl0aBCwAAAAAAAC6NJYoAALgRDzYeBQAAcFvkerZR4AIAwK2Q9AAAALgvcj1bWKIIAAAAAAAAl8YMLgAA3Aif6QEAALgvcj3bKHABAOBGuHU0AACA+yLXs40ligAAAAAAAHBpzOACAMCt8KkeAACA+yLXs4UCFwAAboSUBwAAwH2R69nGEkUAAAAAAAC4NGZwAQDgVvhcDwAAwH2R69lCgQsAADfCnXUAAADcF7mebSxRBAAAAAAAgEujwAUAAAAAAACXxhJFAADciAf7MgAAALgtcj3bPAzDMBwdBAAAsI+8ovN2Hc/Py9+u4wEAAODq2TPXc7c8jyWKcAiz2azx48fLbDY7OhTApfFewqX8vPzt+gCAK8XvJsA+eC+hLOR5tjGDCw6RnZ0tk8mkrKwsBQUFOTocwGXxXgIAOBt+NwH2wXsJuDLM4AIAAAAAAIBLo8AFAAAAAAAAl0aBCwAAAAAAAC6NAhccwtfXV/Hx8fL19XV0KIBL470EAHA2/G4C7IP3EnBl2GQeAAAAAAAALo0ZXAAAAAAAAHBpFLgAAAAAAADg0ihwAQAAAAAAwKVR4EKly8vL0xNPPKHg4GDVqVNHb7zxhqNDAlya2WxWixYttGnTJkeHAgC4zpHnAfZFngdUXBVHB4Drz+jRo7Vjxw5t2LBB6enpio6OVsOGDdW7d29Hhwa4nLy8PD366KNKTk52dCgAAJDnAXZEngdcGQpcqFS5ubl67733tGbNGrVp00Zt2rRRcnKyZs6cSeIDXKEDBw7o0UcfFTfDBQA4A/I8wH7I84ArxxJFVKo9e/aooKBAHTp0sBy7++67tW3bNhUXFzswMsD1fPnll7r33nu1ZcsWR4cCAAB5HmBH5HnAlWMGFyrViRMnFBoaKh8fH8uxWrVqKS8vT6dPn1bNmjUdGB3gWmJiYhwdAgAAFuR5gP2Q5wFXjhlcqFTnz5+Xr6+v1bGLz81msyNCAgAAgB2Q5wEAHIkCFyqVn59fqQTn4nN/f39HhAQAAAA7IM8DADgSBS5Uqnr16ikjI0OFhYWWYydPnlTVqlUVHBzsuMAAAADwh5DnAQAciQIXKlXr1q3l7e2trVu3Wo5t3rxZ7dq1k6cn344AAACuijwPAOBIbDKPSuXv76/o6GgNHTpUCxYs0PHjx/X6669rwYIFjg4NAAAAfwB5HgDAkShwodJNmzZNMTExuvfee2UymTRhwgT16tXL0WEBAADgDyLPAwA4iodhGIajgwAAAAAAAACuFovhAQAAAAAA4NIocAEAAAAAAMClUeACAAAAAACAS6PABQAAAAAAAJdGgQsAAAAAAAAujQIXAAAAAAAAXBoFLgAAAAAAALg0ClwAAAAAAABwaRS4gErUqFEjeXh4WB7e3t6KjIzU9OnT7XqdTp06afz48ZKkQYMGadCgQeWek5+fr3nz5l31NRcuXKhGjRpdcdulxo8fr06dOl11HB4eHtq0adNVnw8AAHA1yPPKR54H4Fqq4ugAgOvN9OnT1bdvX0lSQUGBNmzYoCeeeEIhISEaOHCg3a/31ltvVajfkiVLNGnSJD311FN2jwEAAOB6QJ4HAI7DDC6gkplMJtWuXVu1a9dW/fr1FR0drS5dumjFihXX7Homk6ncfoZhXJPrAwAAXC/I8wDAcShwAU6gSpUq8vHxkVQy7TwuLk5NmjRRgwYNdO7cOR09elQPP/yw/P391ahRI02YMEFFRUWW81euXKmIiAgFBARo2LBhVm2XTl3/6KOPFBkZKX9/f3Xo0EG7d+/Wpk2b9Pjjjys9PV0eHh766aefZBiGJk6cqLp16yo4OFgPPfSQjhw5Yhnn559/1p///GcFBASoTZs2+uGHHyr8ehMSEnTrrbfKz89PwcHB6t+/v3JyciztBQUFevLJJ+Xv768bb7xRS5cutbSVFxcAAIAzIc8jzwNQOShwAQ5UUFCgFStWaN26derRo4fl+IIFC/TRRx9p5cqVqlatmnr16qWwsDDt3r1bCxcu1OLFizV58mRJ0oEDBxQVFaWYmBjt3LlTBQUF2rx5c5nXW7t2rQYPHqznnntOe/fuVdu2bfXggw+qQ4cOmj59um644QadOHFC9evX18yZM/Xxxx9r8eLF2rp1q2rVqqWuXbuqoKBAktS7d28VFRVp+/btGjNmTIX3l/jhhx/Uu3dvPfPMM0pJSdHSpUuVmJiouXPnWvp88803kqRdu3YpJiZGjz76qNLS0iSp3LgAAACcAXkeeR6ASmYAqDQNGzY0fH19jYCAACMgIMDw9PQ0qlWrZjz//POWPvfcc4/Rt29fy/PExESjZs2aRlFRkeVYQkKCERISYhiGYYwaNcq47777LG35+flG3bp1jfj4eMMwDCM6OtqIjo42DMMwevbsafm/YRiG2Ww2Ro4caZw4ccJYsGCB0bBhQ0vbDTfcYCQkJFieFxYWGjVr1jQSEhKM/fv3G5KM9PR0S/vo0aOtzv+934+dmppqzJ4926q9X79+xuDBgw3DMIz4+Hijbt26Rn5+vqW9U6dOxpgxY8qNyzAMQ5KxcePGMuMAAAC4VsjzyPMAOBabzAOV7NVXX1WvXr0kSX5+fqpTp468vLys+vz+TjQHDx7U6dOnFRQUZDlWXFysCxcu6PTp0zpw4IBat25tafP29rZ6/nvff/+9hg4dannu4+Oj119/vVS/nJwcHTt2TH379pWn528TPS9cuKDU1FTl5eUpJCREDRo0sLS1a9fOaoq5LU2bNpWvr68mTZqk/fv3Kzk5WcnJyRowYIClT+vWreXt7W153qZNGx08eLDcuAAAAByJPI88D4DjUOACKllYWJjCw8Mv28fPz8/y/8LCQkVGRmrVqlWl+l3cVNS4ZOPQi/s8XOr3ycTlFBYWSpKWLVumZs2aWbWFhIRo/fr1Fb7mpfbs2aO7775bDz/8sDp27KgRI0aUmvZ+aSJYXFwsHx+fcuMCAABwJPI88jwAjsMeXICTa9asmY4cOaKaNWsqPDxc4eHhOnz4sOLj4+Xh4aEWLVro22+/tfQvLi7Wnj17yhyradOmVm1FRUVq3LixkpKS5OHhYTkeHByssLAwnTx50nLNBg0a6Pnnn9f333+vFi1aKDMz07JfgiTt3r27Qq/nww8/VMeOHfXxxx8rJiZG7dq106FDh6wSqf3791uds337dkVGRpYbFwAAgCshzyPPA2A/FLgAJ9e1a1c1bNhQjz32mPbt26evv/5aQ4YMkb+/v7y8vPTUU09px44dmjRpkr7//nuNGjVK6enpZY4VFxenjz76SIsWLVJaWpr+9re/qbi4WG3atFFAQIAyMzN16NAhFRYWasSIEXrppZf0+eef69ChQ3ryySeVlJSkyMhINW/eXJ07d9bgwYO1d+9erVq1SjNmzKjQ66lRo4b27t2r7du3KzU1VSNHjtS3334rs9ls6ZOenq64uDgdPHhQEydO1K5duyxT7i8XFwAAgCshzyPPA2A/FLgAJ+fl5aWEhAQVFxfrjjvu0COPPKLu3bvr7bffliSFh4crISFBS5YsUevWrXXixAl17969zLE6duyod955R6+++qpatWql7777Tl988YWqVq2q++67T+Hh4WrZsqW+++47jRo1Sk8++aSGDBmi1q1bKz09XWvXrlX16tUlSZ9++qlCQ0N155136oUXXtCzzz5bodczfPhw3XnnnerSpYvuvvtupaen65VXXrH6ZLB79+46ffq02rRpo8WLFyshIUH16tWTpHLjAgAAcBXkeeR5AOzHw7h0gTUAAAAAAADgQpjBBQAAAAAAAJdGgQsAAAAAAAAujQIXAAAAAAAAXBoFLgAAAAAAALg0ClwAAAAAAABwaRS4AAAAAAAA4NIocAEAAAAAAMClUeACAAAAAACAS6PABQAAAAAAAJdGgQsAAAAAAAAujQIXAAAAAAAAXNr/A9r6dUrAMCP0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "        for F, N in zip(\n",
    "                [ features], # statistics_features, time_features, distribution_features, frequency_features, behavior_features, \n",
    "                [ \"All\"], # \"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \n",
    "        ):\n",
    "                X_train = X_train_a[F]\n",
    "                X_test = X_test_a[F] \n",
    "                model = XGBClassifier(\n",
    "                        n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "                        reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "                        colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "                        # subsample=int(best_hyperparams['subsample']),\n",
    "                        # eta=best_hyperparams['eta'],\n",
    "                )\n",
    "                model.fit(X_train, y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "                precision = precision_score(y_test, y_pred)  * 100\n",
    "                recall = recall_score(y_test, y_pred)  * 100\n",
    "                F1 = f1_score(y_test, y_pred)  * 100\n",
    "                if N == \"All\":\n",
    "                    tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\hline\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_f1 = F1\n",
    "                elif N in [\"Statistical-based\", \"Distribution-based\", \"Behavior-based\"]:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                else:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \\\\rowcolor{LigthGray} \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "        if best_results[0] < tmp_f1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (tmp_f1, tmp_str, matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n",
      "\n",
      "\n",
      "92.06349206349206\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.38 & 98.31 & 86.57 & 92.06 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.50 & 91.67 & 94.29 & 92.96 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels\n",
    "    \n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "print(tmp_str)\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DNS_Malware\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6842659, 0.14947642, 0.045675788, 0.091134205, 0.029447665]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PACKETS                     0.000000\n",
       "TIME_DISTRIBUTION           0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000000\n",
       "SPECTRAL_BANDWIDTH          0.000000\n",
       "MEDIAN_SCALED_TIME          0.000000\n",
       "                              ...   \n",
       "VAR                         0.041994\n",
       "HURST_EXPONENT              0.045676\n",
       "MEDIAN_DIFFTIMES            0.089885\n",
       "MEDIAN                      0.220154\n",
       "Q3                          0.315380\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PACKETS': 0.0,\n",
       " 'TIME_DISTRIBUTION': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'MEDIAN_SCALED_TIME': 0.0,\n",
       " 'Q1_SCALED_TIME': 0.0,\n",
       " 'Q3_SCALED_TIME': 0.0,\n",
       " 'DURATION': 0.0,\n",
       " 'MAX_DIFFTIMES': 0.0,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'DIRECTIONS': 0.0,\n",
       " 'TRANSIENTS': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'CNT_DISTRIBUTION': 0.0,\n",
       " 'BIGGEST_CNT_1_SEC': 0.0,\n",
       " 'VAL': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MIN_POWER': 0.0,\n",
       " 'MAX_POWER': 0.0,\n",
       " 'MIN_POWER_FREQ': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'POWER_MEAN': 0.0,\n",
       " 'POWER_STD': 0.0,\n",
       " 'POWER_MODE': 0.0,\n",
       " 'SPECTRAL_ENERGY': 0.0,\n",
       " 'SPECTRAL_ENTROPY': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_SPREAD': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'NORMAL_DISTRIBUTION': 0.0,\n",
       " 'MEAN_SCALED_TIME': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'PACKETS_REV': 0.0,\n",
       " 'BYTES': 0.0,\n",
       " 'BYTES_REV': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'STDEV': 0.0,\n",
       " 'BURSTINESS': 0.0,\n",
       " 'Q1': 0.0,\n",
       " 'MIN': 0.0,\n",
       " 'MAX': 0.0,\n",
       " 'MIN_MINUS_MAX': 0.0,\n",
       " 'MODE': 0.0,\n",
       " 'P_BENFORD': 0.0,\n",
       " 'PERCENT_DEVIATION': 0.0,\n",
       " 'ROOT_MEAN_SQUARE': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'SCALED_ENTROPY': 0.0,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0,\n",
       " 'PERIODICITY_SCDF': 0.0,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_SLOPE': 0.012907388620078564,\n",
       " 'SPECTRAL_KURTOSIS': 0.013205436058342457,\n",
       " 'KURTOSIS': 0.017867257818579674,\n",
       " 'MEAN_DIFFTIMES': 0.019428322091698647,\n",
       " 'SPECTRAL_ROLLOFF': 0.01971523091197014,\n",
       " 'SPECTRAL_CENTROID': 0.022144243121147156,\n",
       " 'SPECTRAL_FLUX': 0.023161906749010086,\n",
       " 'ENTROPY': 0.023962099105119705,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.024193434044718742,\n",
       " 'SWITCHING_METRIC': 0.029447665438055992,\n",
       " 'MIN_DIFFTIMES': 0.04016299545764923,\n",
       " 'AVERAGE_DISPERSION': 0.04071616008877754,\n",
       " 'VAR': 0.04199367016553879,\n",
       " 'HURST_EXPONENT': 0.04567578807473183,\n",
       " 'MEDIAN_DIFFTIMES': 0.08988510817289352,\n",
       " 'MEDIAN': 0.22015365958213806,\n",
       " 'Q3': 0.31537964940071106}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Featuer selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=100):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features, selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:06<00:00, 16.07trial/s, best loss: -0.7476635514018691]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2681571389548412, 'gamma': 6.394774624023922, 'max_depth': 16.0, 'min_child_weight': 9.0, 'n_estimators': 280.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8540477162454725}\n",
      "100%|██████████| 100/100 [00:04<00:00, 20.06trial/s, best loss: -0.6262626262626263]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7840237952193174, 'gamma': 10.002262384413083, 'max_depth': 26.0, 'min_child_weight': 18.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7656106517903365}\n",
      "100%|██████████| 100/100 [00:03<00:00, 26.01trial/s, best loss: -0.7238095238095238]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3327808805023442, 'gamma': 10.049048449535317, 'max_depth': 42.0, 'min_child_weight': 7.0, 'n_estimators': 200.0, 'reg_alpha': 11.0, 'reg_lambda': 0.7962365490576858}\n",
      "100%|██████████| 100/100 [00:03<00:00, 28.86trial/s, best loss: -0.7818181818181819]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7587555879178205, 'gamma': 15.917387076459155, 'max_depth': 32.0, 'min_child_weight': 1.0, 'n_estimators': 340.0, 'reg_alpha': 25.0, 'reg_lambda': 0.4101867863442348}\n",
      "100%|██████████| 100/100 [00:03<00:00, 29.64trial/s, best loss: -0.5263157894736842]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.06916067286596297, 'gamma': 9.583037481722997, 'max_depth': 24.0, 'min_child_weight': 4.0, 'n_estimators': 60.0, 'reg_alpha': 2.0, 'reg_lambda': 0.7074245992083975}\n",
      "100%|██████████| 100/100 [00:05<00:00, 17.26trial/s, best loss: -0.41379310344827586]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7444903107234371, 'gamma': 1.2976385827946066, 'max_depth': 16.0, 'min_child_weight': 14.0, 'n_estimators': 300.0, 'reg_alpha': 17.0, 'reg_lambda': 0.15352344400344758}\n",
      "100%|██████████| 100/100 [00:03<00:00, 26.05trial/s, best loss: -0.8448275862068965]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2530590986063553, 'gamma': 3.705096832082871, 'max_depth': 40.0, 'min_child_weight': 15.0, 'n_estimators': 240.0, 'reg_alpha': 0.0, 'reg_lambda': 0.08369812234944417}\n",
      "100%|██████████| 100/100 [00:06<00:00, 16.16trial/s, best loss: -0.8571428571428572]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4809709791732488, 'gamma': 13.185746474326677, 'max_depth': 22.0, 'min_child_weight': 4.0, 'n_estimators': 60.0, 'reg_alpha': 8.0, 'reg_lambda': 0.22449770888743317}\n",
      "100%|██████████| 100/100 [00:05<00:00, 16.89trial/s, best loss: -0.9105691056910569]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6477464648565407, 'gamma': 15.94440422770028, 'max_depth': 8.0, 'min_child_weight': 3.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.39423837024456265}\n",
      "100%|██████████| 100/100 [00:03<00:00, 28.42trial/s, best loss: -0.7476635514018691]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.17429563138207366, 'gamma': 1.130657169317547, 'max_depth': 6.0, 'min_child_weight': 12.0, 'n_estimators': 140.0, 'reg_alpha': 2.0, 'reg_lambda': 0.2928145898994924}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DNS_Malware\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}